DLW2 Report:¶

By: Roei Yehezkel, 213138787, Omri Vilan, 213479686:¶

Importing & Extracting the data:¶

In [ ]:
# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,
# THEN FEEL FREE TO DELETE THIS CELL.
# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON
# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR
# NOTEBOOK.
import kagglehub
roeiyehezkel_dlw2test_path = kagglehub.dataset_download('roeiyehezkel/dlw2test')
print('Data source import complete.')

Data source import complete.
In [ ]:
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

/kaggle/input/cnn-model/tensorflow2/default/1/cnn_model.h5
/kaggle/input/dlw2test/sample_submission.csv
/kaggle/input/dlw2test/metadata.csv
In [ ]:
import os

os.environ['KAGGLE_USERNAME'] = 'roeiyehezkel'
os.environ['KAGGLE_KEY'] = '648e50e7a636536870f2b596a2b0afc0'
In [ ]:
!kaggle competitions download -c bgu-i-cant-see-you-but-you-are-reading-a-book

Downloading bgu-i-cant-see-you-but-you-are-reading-a-book.zip to /kaggle/working
100%|█████████████████████████████████████▉| 4.98G/4.98G [03:44<00:00, 28.8MB/s]
100%|██████████████████████████████████████| 4.98G/4.98G [03:44<00:00, 23.8MB/s]
In [ ]:
import zipfile
import os

# Path to ZIP file
zip_path = "bgu-i-cant-see-you-but-you-are-reading-a-book.zip"
extract_dir = "/kaggle/temp/data"
os.makedirs(extract_dir, exist_ok=True)

with zipfile.ZipFile(zip_path, 'r') as zf:
    file_list = zf.namelist()
    for file in file_list:
        zf.extract(file, path=extract_dir)
print(f"Extracted {len(file_list)} files to {extract_dir}")

Extracted 124993 files to /kaggle/temp/data

Importing all packages to be used:¶

In [ ]:
!pip install mlflow

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In [ ]:
### Core Libraries
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import skew, kurtosis
import mlflow.sklearn
import os
import seaborn as sns

# Torch and PyTorch Lightning
import torch
from torch.utils.data import DataLoader, Dataset
import torch.nn as nn
from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning import LightningDataModule
import torch.optim as optim
import torch.nn.functional as F

# Scikit-learn Libraries
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
from sklearn.metrics import accuracy_score, log_loss,classification_report, confusion_matrix
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split


# MLflow for Logging
import mlflow
import mlflow.pytorch
from pytorch_lightning.loggers import MLFlowLogger
from pytorch_lightning.callbacks import Callback

Part 1: Exploratory Data Analysis (EDA) for the Dataset¶

The data represents human activity recognition (HAR) using sensor measurements from smart devices and Vicon motion-capture systems. Sensors collect data for different body parts, sides (left/right), and activities performed by users.

Distribution of Activities (Class Imbalance Check): Plot a bar chart to show the frequency of each activity in the dataset.¶

In [ ]:
# Load the train.csv dataset
train = pd.read_csv('/kaggle/temp/data/train.csv')

# Plot the activity distribution
plt.figure(figsize=(10, 6))
train['activity'].value_counts().plot(kind='bar')
plt.title('Activity Distribution')
plt.xlabel('Activity')
plt.ylabel('Frequency')
plt.xticks(rotation=45)
plt.show()
No description has been provided for this image

Sample Time Series Data: Plot the acceleration (x, y, z) for a specific subject and activity.¶

In [ ]:
# Load a sample file (type #1 or type #2)
sample_data = pd.read_csv('/kaggle/temp/data/unlabeled/unlabeled/1.csv')

# Filter for acceleration data (type #1 only)
acceleration_data = sample_data[sample_data['measurement type'] == 'acceleration [m/s/s]']

# Plot acceleration data
plt.figure(figsize=(12, 6))
plt.plot(acceleration_data['x'], label='X-axis')
plt.plot(acceleration_data['y'], label='Y-axis')
plt.plot(acceleration_data['z'], label='Z-axis')
plt.title('Sample Acceleration Data')
plt.xlabel('Time Step')
plt.ylabel('Acceleration (m/s^2)')
plt.legend()
plt.show()
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Columns Overview¶

The training dataset contains the following columns:

  • userid: Identifies the user performing the activity. Will serve us for train-test split.
  • activity: The target label describing the activity being performed. Our target feature
  • sensor: Type of sensor capturing the data (e.g., smartwatch, vicon). There are 2 types:
    • Smartwatch: Measurment type & acceleration in the x, y & z axes.
    • Vicon: Locations in x, y & z axes.
  • body_part: Body location where the sensor is placed (e.g., hand, foot).
  • side: Specifies the side of the body (right/left).
  • id: Unique identifier for each sample. Each id identifies the corresponding csv file of the activity in training data. Csv files without corresponding id in train.csv file refered as the test data.
  • sequence_len: Length of the recorded sequence (time steps). It symbolize the number of rows for corresponding csv file.

The data is time series and categorical, with categories being activities. It represents human activity recognition (HAR) based on sensor readings.

Variability Across Activities: Use boxplots to show the range of acceleration values for each activity.¶

In [ ]:
# Aggregate and visualize acceleration data across activities
plt.figure(figsize=(10, 6))
train['userid'].value_counts().plot(kind='bar')
plt.title('Number of Records per User')
plt.xlabel('User ID')
plt.ylabel('Count')
plt.show()
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Activity distribution between users:¶

In [ ]:
print(train['activity'].value_counts())

activity
walking_freely                   4578
walking_with_handbag             4510
walking_holding_a_tray           4441
walking_with_object_underarm     4412
using_phone                      4313
walking_with_hands_in_pockets    4243
idle                             2675
using_remote_control             2552
reading_book                     2508
washing_mug                      2504
washing_plate                    2502
washing_face_and_hands           2490
preparing_sandwich               2242
brushing_teeth                   1435
stairs_up                        1256
writing                          1233
typing                           1199
stairs_down                      1155
Name: count, dtype: int64
In [ ]:
# Group by 'activity' and 'userid' and count occurrences
activity_user_df = train.groupby(['activity', 'userid']).size().unstack(fill_value=0)
# Get the list of unique user IDs
user_ids = train['userid'].unique()
for user in user_ids:
    user_data = train[train['userid'] == user]

    # Print the activity distribution for the user
    print(f"Activity Distribution for {user}:")
    print(user_data['activity'].value_counts())
    print("-" * 50)

    # Plot the activity distribution for the user
    plt.figure(figsize=(10, 6))
    user_data['activity'].value_counts().plot(kind='bar')
    plt.title(f'Activity Distribution for {user}')
    plt.xlabel('Activity')
    plt.ylabel('Frequency')
    plt.xticks(rotation=45)
    plt.show()

Activity Distribution for user01:
activity
stairs_up                        990
stairs_down                      917
walking_with_handbag             536
walking_with_object_underarm     507
walking_freely                   497
walking_holding_a_tray           494
walking_with_hands_in_pockets    493
using_phone                      486
using_remote_control             346
reading_book                     297
idle                             290
washing_plate                    285
washing_mug                      265
washing_face_and_hands           254
brushing_teeth                   225
preparing_sandwich               199
writing                          161
typing                           145
Name: count, dtype: int64
--------------------------------------------------
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Activity Distribution for user08:
activity
walking_freely                   566
walking_with_object_underarm     562
walking_holding_a_tray           556
walking_with_handbag             549
walking_with_hands_in_pockets    538
using_phone                      522
idle                             344
reading_book                     317
washing_mug                      304
using_remote_control             297
washing_plate                    295
washing_face_and_hands           282
preparing_sandwich               193
writing                          150
typing                           141
brushing_teeth                   139
Name: count, dtype: int64
--------------------------------------------------
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Activity Distribution for user07:
activity
walking_freely                   617
walking_holding_a_tray           608
using_phone                      603
walking_with_handbag             599
walking_with_object_underarm     585
walking_with_hands_in_pockets    568
idle                             423
reading_book                     361
washing_mug                      356
washing_plate                    336
preparing_sandwich               315
using_remote_control             236
washing_face_and_hands           231
brushing_teeth                   192
writing                          152
typing                           149
Name: count, dtype: int64
--------------------------------------------------
No description has been provided for this image 
Activity Distribution for user02:
activity
walking_freely                   582
walking_with_handbag             573
walking_with_object_underarm     561
walking_holding_a_tray           560
walking_with_hands_in_pockets    542
using_phone                      522
washing_face_and_hands           398
preparing_sandwich               340
using_remote_control             320
idle                             296
reading_book                     295
washing_mug                      280
washing_plate                    261
typing                           164
brushing_teeth                   154
writing                          146
stairs_up                         61
stairs_down                       47
Name: count, dtype: int64
--------------------------------------------------
No description has been provided for this image 
Activity Distribution for user05:
activity
walking_freely                   594
walking_with_handbag             572
walking_with_object_underarm     566
walking_holding_a_tray           558
walking_with_hands_in_pockets    548
using_phone                      536
idle                             330
washing_face_and_hands           319
using_remote_control             313
washing_plate                    309
washing_mug                      302
preparing_sandwich               294
reading_book                     255
brushing_teeth                   207
writing                          156
stairs_up                        142
typing                           134
stairs_down                      127
Name: count, dtype: int64
--------------------------------------------------
No description has been provided for this image 
Activity Distribution for user03:
activity
walking_freely                   591
walking_with_object_underarm     570
walking_with_handbag             554
using_phone                      537
walking_holding_a_tray           534
walking_with_hands_in_pockets    526
preparing_sandwich               431
washing_plate                    386
washing_face_and_hands           368
washing_mug                      365
idle                             351
using_remote_control             343
reading_book                     336
writing                          215
brushing_teeth                   197
typing                           188
Name: count, dtype: int64
--------------------------------------------------
No description has been provided for this image 
Activity Distribution for user06:
activity
walking_freely                   577
walking_holding_a_tray           567
using_phone                      537
walking_with_handbag             516
walking_with_hands_in_pockets    468
walking_with_object_underarm     459
using_remote_control             335
reading_book                     326
idle                             324
washing_mug                      301
washing_face_and_hands           298
washing_plate                    285
preparing_sandwich               161
typing                           143
brushing_teeth                   140
writing                          109
stairs_up                         13
stairs_down                       12
Name: count, dtype: int64
--------------------------------------------------
No description has been provided for this image 
Activity Distribution for user04:
activity
walking_with_handbag             611
walking_with_object_underarm     602
using_phone                      570
walking_holding_a_tray           564
walking_with_hands_in_pockets    560
walking_freely                   554
using_remote_control             362
washing_plate                    345
washing_face_and_hands           340
washing_mug                      331
reading_book                     321
idle                             317
preparing_sandwich               309
brushing_teeth                   181
writing                          144
typing                           135
stairs_down                       52
stairs_up                         50
Name: count, dtype: int64
--------------------------------------------------
No description has been provided for this image
In [ ]:
activity_user_df
Out[ ]:
userid user01 user02 user03 user04 user05 user06 user07 user08
activity
brushing_teeth 225 154 197 181 207 140 192 139
idle 290 296 351 317 330 324 423 344
preparing_sandwich 199 340 431 309 294 161 315 193
reading_book 297 295 336 321 255 326 361 317
stairs_down 917 47 0 52 127 12 0 0
stairs_up 990 61 0 50 142 13 0 0
typing 145 164 188 135 134 143 149 141
using_phone 486 522 537 570 536 537 603 522
using_remote_control 346 320 343 362 313 335 236 297
walking_freely 497 582 591 554 594 577 617 566
walking_holding_a_tray 494 560 534 564 558 567 608 556
walking_with_handbag 536 573 554 611 572 516 599 549
walking_with_hands_in_pockets 493 542 526 560 548 468 568 538
walking_with_object_underarm 507 561 570 602 566 459 585 562
washing_face_and_hands 254 398 368 340 319 298 231 282
washing_mug 265 280 365 331 302 301 356 304
washing_plate 285 261 386 345 309 285 336 295
writing 161 146 215 144 156 109 152 150
In [ ]:
#maximal and minimal values of sequence_length from train df per sensor(smartwatch or vicon):
print(train.groupby('sensor')['sequence_length'].max())
print(train.groupby('sensor')['sequence_length'].min())

sensor
smartwatch    4000
vicon         4000
Name: sequence_length, dtype: int64
sensor
smartwatch    3000
vicon         3000
Name: sequence_length, dtype: int64

*There are 18 different subjects(activities) in the data, and 8 different users.

  • Activities like stairs_down and stairs_up are significantly underrepresented for some users (e.g., user03, user07, user08).
  • Other activities like walking_freely and walking_with_handbag have a relatively balanced distribution across all users.
  • Some activities (e.g. using_phone, walking_freely) occur more often then others(e.g. writing, brushing_teeth) accross all users.

Detailed Insights on Train-Test Split¶

Train Dataset¶

  • Overview:

    • Contains 50,248 rows and 8 columns.
    • Includes information such as userid, activity (target labels), sensor, body_part, side, id, and sequence_length.

  • Users Included:

    • The train dataset comprises data from 8 unique users: user01, user02, user03, user04, user05, user06, user07, user08.

  • Activities:

    • Each row represents a specific activity performed by one of the users, recorded using sensors.

  • Sequence Length:

    • The number of time steps per sequence varies, as indicated by the sequence_length column.

Test Dataset (Metadata File)¶

  • Overview:

    • Metadata consists of 74,744 rows and 6 columns.
    • Columns include sample_id, userid, sensor, body_part, side, and sequence_length.

  • Users Included:

    • The metadata file encompasses data from 21 unique users: user01 to user21.

  • Distinct Users:

    • Users user09 to user21 are present in the metadata but absent in the training dataset.
    • This indicates that the test dataset includes sequences from entirely new users not present in the training dataset.

  • Sequence Length:

    • Similar to the train dataset, sequences have varying lengths.

Train-Test Split Observations¶

  1. Generalization to New Users:

    • The train-test split ensures no overlap of users between the training and test datasets.
    • This design tests the model's ability to generalize to unseen users, which is critical for real-world applications of human activity recognition.

  2. User-Based Segregation:

    • Training data includes activities from a fixed set of users (user01 to user08), while the test dataset focuses on sequences from new users (user09 to user21).

  3. Challenge for the Model:

    • The lack of overlap in users introduces variability in how activities are performed (e.g., gait, movement style), making the test dataset significantly different from the training data.

Possible Self-Supervised Tasks¶

1. Masking for Pretraining¶

  • Description:
    • Mask portions of the input sequences (e.g., certain time steps or specific sensor axes).
    • Train the model to predict the masked portions based on the surrounding context.
  • Advantages:
    • Forces the model to learn meaningful representations of the data.
    • Captures temporal and contextual dependencies, which are crucial for time-series tasks.
  • Application in Activity Recognition:
    • Mask random time steps or sensor axes (e.g., mask the y axis of acceleration).
    • Train the model to reconstruct the full sequence, thereby learning robust embeddings for activities.

2. Autoencoder for Representation Learning¶

  • Description:
    • Use an autoencoder architecture where the encoder compresses the input sequence into a lower-dimensional latent space, and the decoder reconstructs the input from the latent representation.
  • Advantages:
    • Learns compact and noise-robust representations of the data.
    • Provides a natural way to denoise and reduce dimensionality in time-series data.
  • Application in Activity Recognition:
    • Train the autoencoder on raw sensor sequences to capture general activity patterns.
    • Use the encoder's output as input features for downstream activity classification tasks.
In [ ]:
train = pd.read_csv('/kaggle/temp/data/train.csv')
train['userid'] = train['userid'].apply(lambda x: int(x[-2:]))
train = pd.get_dummies(train, columns=['sensor', 'body_part', 'side'])
for col in ['sensor_smartwatch','sensor_vicon', 'body_part_hand','body_part_foot', 'side_right', 'side_left']:
    train[col] = train[col].astype(int)
print(train['sequence_length'])
max_len = max(list(train['sequence_length']))
print(max_len)
# train.drop(columns=['sequence_length'])
encoder = LabelEncoder()
train['activity_enc'] = encoder.fit_transform(train['activity'])
labels_dict = {row[1]['id'] : row[1]['activity_enc'] for row in train.iterrows()}

0        3000
1        4000
2        3000
3        3000
4        4000
         ... 
50243    3500
50244    3000
50245    3000
50246    4000
50247    4000
Name: sequence_length, Length: 50248, dtype: int64
4000

Naive Baseline solution:¶

For naive baseline solution, we will use the class distribution for each category as naïve prediction:

In [ ]:
# Function to extract statistical features from a sequence
def extract_statistical_features(sequence):
    features = {}
    for col in ["x", "y", "z"]:
        features[f"{col}_mean"] = sequence[col].mean()
        features[f"{col}_std"] = sequence[col].std()
        features[f"{col}_skew"] = skew(sequence[col])
        features[f"{col}_kurtosis"] = kurtosis(sequence[col])
    return features

# Function to calculate Signal Magnitude Area (SMA)
def calculate_sma(sequence):
    sma = np.sum(np.abs(sequence[["x", "y", "z"]]), axis=1).mean()
    return {"sma": sma}

# Function to calculate zero-crossing rate
def calculate_zero_crossing_rate(sequence):
    zcr = {}
    for col in ["x", "y", "z"]:
        zcr[f"{col}_zcr"] = ((sequence[col][1:].values * sequence[col][:-1].values) < 0).mean()
    return zcr

# Function to process sensor data and unify features
def process_sensor_data(sequence, metadata_row):
    # Extract statistical features
    stats_features = extract_statistical_features(sequence)

    # Calculate SMA
    sma_features = calculate_sma(sequence)

    # Calculate zero-crossing rate
    zcr_features = calculate_zero_crossing_rate(sequence)

    # Combine all features
    features = {**stats_features, **sma_features, **zcr_features}

    # Add metadata features (e.g., right vs. left, hand vs. foot)
    for col in ["sensor_smartwatch", "sensor_vicon", "body_part_hand", "body_part_foot", "side_right", "side_left"]:
        features[col] = metadata_row[col]

    # Add sequence length as a feature
    features["sequence_length"] = len(sequence)

    return features

# Main function to process all sequences
def process_sequences(data, dfs):
    feature_list = []
    labels = []

    for ID in data["id"].values:
        if ID in dfs:
            sequence = dfs[ID]
            metadata_row = data[data["id"] == ID].iloc[0]

            # Process the sequence and extract features
            features = process_sensor_data(sequence, metadata_row)
            feature_list.append(features)

            # Add the corresponding label
            labels.append(metadata_row["activity"])

    # Convert to DataFrame
    feature_df = pd.DataFrame(feature_list)
    return feature_df, labels

# Preprocess the data directory
directory = '/kaggle/temp/data/unlabeled/unlabeled/'
columns = ['sensor_smartwatch', 'sensor_vicon', 'body_part_hand', 'body_part_foot', 'side_right', 'side_left']
dfs = {}
train_ids = train['id'].values  # Get all train IDs

for filename in os.listdir(directory):
    if filename.endswith('.csv'):  # Check if the file is a CSV
        file_path = os.path.join(directory, filename)
        file_id = int(filename[:-4])  # Extract the file ID from the filename
        type = 0

        # Process only files where file_id is in train_ids
        if file_id in train_ids:
            try:
                # Read the CSV file
                df = pd.read_csv(file_path)

                # Check if 'measurement type' column exists
                if 'measurement type' in df.columns:
                    type = 1
                    # Filter for acceleration data
                    df = df[df['measurement type'] == 'acceleration [m/s/s]']

                    # Add relevant columns based on train DataFrame
                    for col in columns:
                        val = train[train['id'] == file_id][col].values
                        if len(val) > 0:
                            df[col] = val[0]

                    # Drop the 'measurement type' column
                    df.drop(columns=['measurement type'], inplace=True)
                else:
                    type = 2
                    # Rename columns for location data
                    df.rename(columns={
                        'x [m]': 'x',
                        'y [m]': 'y',
                        'z [m]': 'z'
                    }, inplace=True)

                    # Add relevant columns based on train DataFrame
                    for col in columns:
                        val = train[train['id'] == file_id][col].values
                        if len(val) > 0:
                            df[col] = val[0]

                # Store the DataFrame in the dictionary
                dfs[file_id] = df

            except Exception as e:
                print(f"Error processing file {file_path}: {e}")

# Check how many files were loaded
print(f"Loaded {len(dfs)} files.")

# Perform a regular train-test split
train_set, val_set = train_test_split(
    train,
    test_size=0.2,  # Adjust test size as needed (e.g., 20% for validation)
    stratify=train['activity'],  # Ensure label distribution is maintained
    random_state=42  # For reproducibility
)

# Process train and validation sequences
train_features, train_labels = process_sequences(train_set, dfs)
val_features, val_labels = process_sequences(val_set, dfs)

Loaded 50248 files.
In [ ]:
from sklearn.metrics import log_loss
import numpy as np

# Calculate class distribution in the training set
class_counts = train_set['activity'].value_counts()
class_probabilities = class_counts / class_counts.sum()

# Convert to a dictionary for easier mapping
class_distribution = class_probabilities.to_dict()

# Create a DataFrame with predicted probabilities for the validation set
val_predictions = pd.DataFrame(
    np.tile(list(class_distribution.values()), (len(val_set), 1)),
    columns=class_distribution.keys()
)

# Encode true labels for log-loss calculation
label_encoder = LabelEncoder()
train_set['activity_enc'] = label_encoder.fit_transform(train_set['activity'])
val_set['activity_enc'] = label_encoder.transform(val_set['activity'])

# Calculate log-loss for the validation set
val_logloss = log_loss(val_set['activity_enc'], val_predictions)

print(f"Validation Log-Loss for Naïve Baseline: {val_logloss:.4f}")

Validation Log-Loss for Naïve Baseline: 3.0516

Classical ML Solution:¶

For classical ML solution, we will use random forest with 100 classifiers

In [ ]:
train_features.columns
Out[ ]:
Index(['x_mean', 'x_std', 'x_skew', 'x_kurtosis', 'y_mean', 'y_std', 'y_skew',
       'y_kurtosis', 'z_mean', 'z_std', 'z_skew', 'z_kurtosis', 'sma', 'x_zcr',
       'y_zcr', 'z_zcr', 'sensor_smartwatch', 'sensor_vicon', 'body_part_hand',
       'body_part_foot', 'side_right', 'side_left', 'sequence_length'],
      dtype='object')
In [ ]:
# Start an MLflow run
with mlflow.start_run():
    # Ensure labels are encoded
    label_encoder = LabelEncoder()
    train_encoded_labels = label_encoder.fit_transform(train_labels)
    val_encoded_labels = label_encoder.transform(val_labels)

    # Train Random Forest Classifier
    rf_model = RandomForestClassifier(random_state=42, n_estimators=100)
    rf_model.fit(train_features, train_encoded_labels)

    # Predictions and Metrics
    train_probabilities = rf_model.predict_proba(train_features)
    val_probabilities = rf_model.predict_proba(val_features)

    train_logloss = log_loss(train_encoded_labels, train_probabilities)
    val_logloss = log_loss(val_encoded_labels, val_probabilities)

    train_predictions = rf_model.predict(train_features)
    val_predictions = rf_model.predict(val_features)

    train_accuracy = accuracy_score(train_encoded_labels, train_predictions)
    val_accuracy = accuracy_score(val_encoded_labels, val_predictions)

    # Log parameters
    mlflow.log_param("model", "Random Forest")
    mlflow.log_param("n_estimators", 100)
    mlflow.log_param("random_state", 42)

    # Log metrics
    mlflow.log_metric("train_accuracy", train_accuracy)
    mlflow.log_metric("val_accuracy", val_accuracy)
    mlflow.log_metric("train_log_loss", train_logloss)
    mlflow.log_metric("val_log_loss", val_logloss)

    # Log classification report as an artifact
    val_classification_report = classification_report(val_encoded_labels, val_predictions)
    with open("/kaggle/working/classification_report.txt", "w") as f:
        f.write(val_classification_report)
    mlflow.log_artifact("/kaggle/working/classification_report.txt")

    # Log the trained model
    mlflow.sklearn.log_model(rf_model, "random_forest_model")

# Print metrics to console
print(f"Validation Accuracy (Random Forest): {val_accuracy:.4f}")
print(f"Training Log Loss: {train_logloss:.4f}")
print(f"Validation Log Loss: {val_logloss:.4f}")

2025/01/04 07:02:10 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.

Validation Accuracy (Random Forest): 0.9576
Training Log Loss: 0.0908
Validation Log Loss: 0.3300
In [ ]:
# Compute the confusion matrix
conf_matrix = confusion_matrix(val_encoded_labels, val_predictions)

# Get label names from the label encoder
label_names = label_encoder.classes_

# Plot the confusion matrix
plt.figure(figsize=(10, 8))
sns.heatmap(conf_matrix, annot=True, fmt="d", cmap="Blues", xticklabels=label_names, yticklabels=label_names)
plt.xlabel("Predicted Label")
plt.ylabel("Actual Label")
plt.title("Confusion Matrix")
plt.show()
No description has been provided for this image

Validation Strategy¶

Chosen Validation Strategy¶

The validation strategy for the LSTM model involves splitting the labeled dataset into training and validation sets based on user IDs. This ensures that sequences from the same user do not appear in both training and validation sets, which prevents data leakage and provides a realistic evaluation of the model's ability to generalize to unseen users.

Key Elements of the Validation Strategy¶

  1. Split by User IDs:

    • Extracts unique user IDs from the dataset.
    • Splits the IDs into training and validation sets using an 80/20 ratio while maintaining a fixed random seed for reproducibility.
    • Filters the training and validation datasets based on these user IDs.

  2. Preprocessing of Sequences:

    • Each sequence is processed to extract statistical features (e.g., mean, standard deviation, skewness, kurtosis) for the x, y, and z axes of the sensor data.
    • Additional features include Signal Magnitude Area (SMA) and Zero-Crossing Rate (ZCR).
    • Metadata features (e.g., sensor type, body part, and side) are also included for better context.

  3. Sequence Labeling:

    • Each sequence is assigned its corresponding label, which is encoded for compatibility with the model.

  4. Reproducibility:

    • A fixed random seed ensures the train-test split is consistent across experiments, facilitating comparison of results.

Advantages of the Strategy¶

  1. Generalization:

    • Splitting by user IDs ensures that the model is tested on entirely unseen users, providing a robust evaluation of generalization.

  2. Feature Extraction:

    • Statistical features and domain-specific metrics (SMA, ZCR) enhance the model's ability to capture temporal dependencies in sensor data.

  3. Avoids Data Leakage:

    • By isolating user IDs between training and validation sets, sequences from the same user cannot influence both training and validation results.

CNN:¶

Summary of the 1D-CNN Model Features¶

Model Architecture¶

1. Layer-by-Layer Description:¶

  1. Input Layer:

    • Accepts preprocessed sequence data of shape (batch_size, sequence_length, input_channels).

  2. Convolutional Layers:

    • Conv1D Layer 1:

      • Kernel size: 5
      • Number of filters: 64
      • Stride: 1
      • Activation: ReLU

    • Max Pooling Layer 1:

      • Pool size: 2
      • Reduces the temporal dimension by half.

    • Conv1D Layer 2:

      • Kernel size: 3
      • Number of filters: 128
      • Stride: 1
      • Activation: ReLU

    • Max Pooling Layer 2:

      • Pool size: 2
      • Further reduces the temporal dimension.

    • Conv1D Layer 3:

      • Kernel size: 3
      • Number of filters: 256
      • Stride: 1
      • Activation: ReLU

    • Global Average Pooling:

      • Averages feature maps across the temporal dimension to produce a fixed-length feature vector.

  3. Fully Connected Layers:

    • Dense Layer 1:
      • Units: 128
      • Activation: ReLU
      • Dropout: 0.5 (to reduce overfitting).
    • Output Layer:
      • Units: Equal to the number of classes in the dataset.
      • Activation: Softmax (for multi-class classification).

Additional Details:¶

  • Kernel Design:

    • Larger kernels (e.g., 5) in initial layers to capture broader temporal features.
    • Smaller kernels (e.g., 3) in deeper layers for fine-grained feature extraction.

  • Dropout Regularization:

    • Dropout applied after dense layers to prevent overfitting and improve generalization.

  • Final Output:

    • Produces class probabilities for each input sequence.

Training and Optimization¶

  1. Loss Function:

    • Cross-Entropy Loss for multi-class classification.

  2. Optimizer:

    • Adam optimizer with:
      • Learning rate: 0.001
      • Weight decay for regularization.

  3. Metrics:

    • Logs train_loss, val_loss, and val_acc for every epoch.

  4. Early Stopping:

    • Monitors val_loss with a patience of 5 epochs.

  5. Model Checkpointing:

    • Saves the best model based on the lowest val_loss.

Data Handling¶

  1. Input Features:

    • Time-series data normalized using Min-Max scaling.
    • Each sequence reshaped to fit the input dimensions of (batch_size, sequence_length, input_channels).

  2. Batching:

    • Utilizes PyTorch DataLoader for efficient batch processing.

  3. Collate Function:

    • Custom collate function ensures uniform input dimensions within batches.

Logging and Monitoring¶

  1. MLFlow Integration:

    • Logs hyperparameters such as kernel_size, num_filters, learning_rate, and dropout.
    • Tracks key metrics like training/validation loss and accuracy.
    • Saves the best-performing model and logs for experiment tracking.

  2. Metrics Tracking:

    • Records training/validation losses and accuracies for visualization.

Key Features¶

  • Localized Feature Learning:
    • Convolutional layers capture local temporal dependencies in time-series data.
  • Scalability:
    • Flexible architecture supports varying sequence lengths and dataset sizes.
  • Regularization:
    • Dropout layers mitigate overfitting.
  • Early Stopping and Checkpoints:
    • Ensures optimal performance by halting training early and saving the best model.
  • Integrated Logging:
    • Tracks experiments seamlessly via MLFlow.

Pre-processing:¶

In [ ]:
import numpy as np
import pandas as pd
from scipy.stats import skew, kurtosis
from sklearn.model_selection import train_test_split
import torch
import mlflow.sklearn
from sklearn.metrics import classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, log_loss

# Function to extract statistical features from a sequence
def extract_statistical_features(sequence):
    features = {}
    for col in ["x", "y", "z"]:
        features[f"{col}_mean"] = sequence[col].mean()
        features[f"{col}_std"] = sequence[col].std()
        features[f"{col}_skew"] = skew(sequence[col])
        features[f"{col}_kurtosis"] = kurtosis(sequence[col])
    return features

# Function to calculate Signal Magnitude Area (SMA)
def calculate_sma(sequence):
    sma = np.sum(np.abs(sequence[["x", "y", "z"]]), axis=1).mean()
    return {"sma": sma}

# Function to calculate zero-crossing rate
def calculate_zero_crossing_rate(sequence):
    zcr = {}
    for col in ["x", "y", "z"]:
        zcr[f"{col}_zcr"] = ((sequence[col][1:].values * sequence[col][:-1].values) < 0).mean()
    return zcr

# Function to process sensor data and unify features
def process_sensor_data(sequence):
    # Extract statistical features
    stats_features = extract_statistical_features(sequence)

    # Calculate SMA
    sma_features = calculate_sma(sequence)

    # Calculate zero-crossing rate
    zcr_features = calculate_zero_crossing_rate(sequence)

    # Combine all features
    features = {**stats_features, **sma_features, **zcr_features}

    # # Add metadata features (e.g., right vs. left, hand vs. foot)
    # for col in ["sensor_smartwatch", "sensor_vicon", "body_part_hand", "body_part_foot", "side_right", "side_left"]:
    #     features[col] = metadata_row[col]

    # Add sequence length as a feature
    # features["sequence_length"] = len(sequence)

    return features

# Main function to process all sequences
def process_sequences(data, filename, id_list, directory):
    if filename.endswith('.csv'):  # Check if the file is a CSV
        file_path = os.path.join(directory, filename)
        file_id = int(filename[:-4])  # Extract the file ID from the filename
        type = 0
        # Process only files where file_id is in train_ids
        if file_id in id_list:
            try:
                # Read the CSV file
                df = pd.read_csv(file_path)

                # Check if 'measurement type' column exists
                if 'measurement type' in df.columns:
                    type = 1
                    # Filter for acceleration data
                    df = df[df['measurement type'] == 'acceleration [m/s/s]']

                    # Add relevant columns based on train DataFrame
                    for col in columns:
                        val = train[train['id'] == file_id][col].values
                        if len(val) > 0:
                            df[col] = val[0]

                    # Drop the 'measurement type' column
                    df.drop(columns=['measurement type'], inplace=True)
                else:
                    type = 2
                    # Rename columns for location data
                    df.rename(columns={
                        'x [m]': 'x',
                        'y [m]': 'y',
                        'z [m]': 'z'
                    }, inplace=True)

                    # Add relevant columns based on train DataFrame
                    for col in columns:
                        val = train[train['id'] == file_id][col].values
                        if len(val) > 0:
                            df[col] = val[0]

            except Exception as e:
                print(f"Error processing file {file_path}: {e}")
                return None

    # Process the sequence and extract features
    features = process_sensor_data(df)
    for col in features.keys():
        df[col] = features[col]
    # Add the corresponding label
    label = labels_dict[file_id]
    seq = torch.tensor(df.values, dtype=torch.float32).permute(1, 0)

    return seq, label

# Preprocess the data directory
directory = '/kaggle/temp/data/unlabeled/unlabeled/'
columns = ['sensor_smartwatch', 'sensor_vicon', 'body_part_hand', 'body_part_foot', 'side_right', 'side_left']


# Perform a regular train-test split
train_set, val_set = train_test_split(
    train,
    test_size=0.2,  # Adjust test size as needed (e.g., 20% for validation)
    stratify=train['activity'],  # Ensure label distribution is maintained
    random_state=42  # For reproducibility
)

train_ids = list(train_set['id'])
val_ids = list(val_set['id'])
In [ ]:
def pad_to_length(sequence, target_length, padding_value=0.0):
        """
        Pads a sequence to a specific target length.
        Args:
            sequence (Tensor): The input tensor sequence of shape (sequence_length, features).
            target_length (int): The target length to pad the sequence to.
            padding_value (float): The value to pad with (default is 0.0).
        Returns:
            Tensor: The padded sequence.
        """
        seq_length = sequence.size(1)
        if seq_length < target_length:
            # Calculate padding
            padding_needed = target_length - seq_length
            # Pad at the end of the sequence (padding_value is the value to use)
            padded_sequence = F.pad(sequence, (0, padding_needed), value=padding_value)
        else:
            # If the sequence is already long enough, no padding is needed
            padded_sequence = sequence[:target_length]  # Optionally truncate if it's too long
        return padded_sequence

Dataset Creation:¶

In [ ]:
class ProcessedSequenceDataset(Dataset):
    def __init__(self, data_dir, labels, id_list, max_len, padding_value=0.0):
        """
        Dataset that preprocesses features (scaling and padding) in the initializer.
        Args:
        - features: List of DataFrames (one per sequence).
        - labels: List of corresponding labels for the sequences.
        - padding_value: Value to use for padding.
        """
        #convert features to tensors
        self.files = [f'{ID}.csv' for ID in id_list]
        self.labels = labels
        self.padding_value = padding_value
        self.id_list = id_list
        self.data_dir = data_dir
        self.max_len = max_len

    def __len__(self):
        return len(self.files)


    def __getitem__(self, idx):
        """
        Get a single sequence and its label.
        """
        filename = self.files[idx]
        seq, label =  process_sequences(train, filename, self.id_list, self.data_dir)
        pad_seq = pad_to_length(seq, self.max_len, self.padding_value)
        return pad_seq, label

data_dir = '/kaggle/temp/data/unlabeled/unlabeled/'

train_dataset = ProcessedSequenceDataset(data_dir,labels_dict, train_ids,max_len)
val_dataset = ProcessedSequenceDataset(data_dir,labels_dict,val_ids, max_len)
In [ ]:
class SequenceDataModule(LightningDataModule):
    def __init__(self, train_dataset, val_dataset, batch_size=32):
        super().__init__()
        self.train_dataset = train_dataset
        self.val_dataset = val_dataset
        self.batch_size = batch_size

    def train_dataloader(self):
        return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=3)

    def val_dataloader(self):
        return DataLoader(self.val_dataset, batch_size=self.batch_size, num_workers=3)

CNN Class:¶

In [ ]:
class CNN1DClassifier(pl.LightningModule):
    def __init__(self, input_channels, num_classes, learning_rate=0.001, dropout_rate=0.3, weight_decay=1e-4):
        super(CNN1DClassifier, self).__init__()
        self.save_hyperparameters()

        # Define the model
        self.conv1 = nn.Conv1d(in_channels=input_channels, out_channels=64, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm1d(64)
        self.conv2 = nn.Conv1d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm1d(128)
        self.conv3 = nn.Conv1d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm1d(256)
        self.dropout = nn.Dropout(p=dropout_rate)
        self.fc1 = nn.Linear(256, 128)
        self.fc2 = nn.Linear(128, num_classes)

        # Define loss function
        self.criterion = nn.CrossEntropyLoss()

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.relu(self.bn3(self.conv3(x)))
        x = torch.mean(x, dim=2)  # Global average pooling
        x = self.dropout(F.relu(self.fc1(x)))
        x = self.fc2(x)
        return x

    def training_step(self, batch, batch_idx):
        sequences, labels = batch
        outputs = self(sequences)
        loss = self.criterion(outputs, labels)
        mlflow.log_metric("train_loss", loss.item(), step=self.global_step)
        return loss

    def validation_step(self, batch, batch_idx):
        sequences, labels = batch
        outputs = self(sequences)
        loss = self.criterion(outputs, labels)
        mlflow.log_metric("val_loss", loss.item(), step=self.global_step)
        _, preds = torch.max(outputs, dim=1)
        acc = (preds == labels).float().mean()
        self.log("val_loss", loss, prog_bar=True)
        mlflow.log_metric("val_acc", acc.item(), step=self.global_step)
        self.log("val_acc", acc, prog_bar=True)

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(
            self.parameters(),
            lr=self.hparams.learning_rate,
            weight_decay=self.hparams.weight_decay
        )
        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=3)
        return {
            "optimizer": optimizer,
            "lr_scheduler": {
                "scheduler": scheduler,
                "monitor": "val_loss",
            },
        }
In [ ]:
val_loader = data_module.val_dataloader()
class MetricsTracker(Callback):
    def __init__(self):
        self.train_losses = []
        self.val_losses = []
        self.val_accuracies = []

    def on_train_epoch_end(self, trainer, pl_module):
        # Get the training loss from the logged metrics
        train_loss = trainer.callback_metrics.get("train_loss")
        if train_loss:
            self.train_losses.append(train_loss.item())

    def on_validation_epoch_end(self, trainer, pl_module):
        # Get the validation loss and accuracy from the logged metrics
        val_loss = trainer.callback_metrics.get("val_loss")
        val_acc = trainer.callback_metrics.get("val_acc")
        if val_loss:
            self.val_losses.append(val_loss.item())
        if val_acc:
            self.val_accuracies.append(val_acc.item())

    def save_metrics_to_file(self, file_path):
        # Save metrics to a text file
        with open(file_path, "w") as f:
            f.write("Training and Validation Metrics\n")
            f.write("=" * 40 + "\n")
            f.write("Epoch, Train Loss, Validation Loss, Validation Accuracy\n")
            for i in range(len(self.train_losses)):
                train_loss = self.train_losses[i] if i < len(self.train_losses) else "N/A"
                val_loss = self.val_losses[i] if i < len(self.val_losses) else "N/A"
                val_acc = self.val_accuracies[i] if i < len(self.val_accuracies) else "N/A"
                f.write(f"{i+1}, {train_loss}, {val_loss}, {val_acc}\n")
        print(f"Metrics saved to {file_path}")

# Setup MLflow
mlflow.set_experiment("CNN_Experiment")

# MLflow Logger
mlflow_logger = MLFlowLogger(
    experiment_name="CNN_Experiment",
    run_name="CNN_1"
)



# Define input dimensions and parameters
max_len = train_dataset.max_len  # Adjust based on dataset
input_dim = max_len  # Number of features (e.g., x, y, z axes)
hidden_dim = 128
num_classes = len(set(labels_dict.values()))  # Number of unique labels
learning_rate = 0.0005

# Initialize Model
# Hyperparameters
input_channels = 25  # Replace with the actual number of input channels
num_classes = 18
batch_size = 64
learning_rate = 0.001
max_epochs = 10

# Model
model = CNN1DClassifier(input_channels=input_channels, num_classes=num_classes, learning_rate=learning_rate)

# DataModule
data_module = SequenceDataModule(train_dataset=train_dataset, val_dataset=val_dataset, batch_size=batch_size)

# trainer = Trainer(accelerator="gpu", devices=1, max_epochs=max_epochs)

# Trainer setup
# Initialize the metrics tracker
metrics_tracker = MetricsTracker()

# Trainer setup
trainer = pl.Trainer(
    max_epochs=25,
    logger=mlflow_logger,
    callbacks=[
        EarlyStopping(monitor='val_loss', patience=5, mode='min'),
        ModelCheckpoint(monitor='val_loss', mode='min', save_top_k=1, filename='best_model'),
        metrics_tracker  # Add metrics tracker callback here
    ],
    accelerator="gpu" if torch.cuda.is_available() else "cpu",
    devices=torch.cuda.device_count()
)

val_loader = data_module.val_dataloader()

with mlflow.start_run():


    # Validate the model
    val_results = trainer.validate(model, val_loader)

    # Log validation results
    for metric in val_results:
        for k, v in metric.items():
            mlflow.log_metric(k, v)

# Save metrics to file
metrics_tracker.save_metrics_to_file("/kaggle/working/metrics_log.txt")


# Visualizations
# Plot training and validation loss
# metrics = trainer.logged_metrics
# train_losses = metrics["train_loss"]
# val_losses = metrics["val_loss"]
train_losses = metrics_tracker.train_losses
validation_losses = metrics_tracker.val_losses

# Pad train_losses to match the length of validation_losses
if len(train_losses) < len(validation_losses):
    padding_value = train_losses[-1] if train_losses else 0  # Use last value or 0 if empty
    train_losses += [padding_value] * (len(validation_losses) - len(train_losses))

metrics_tracker.train_losses = train_losses
# Visualize Training and Validation Loss
plt.figure(figsize=(10, 6))
# plt.plot(metrics_tracker.train_losses, label="Train Loss")
plt.plot(metrics_tracker.val_losses, label="Validation Loss")
plt.title("Loss Over Epochs")
plt.xlabel("Epoch")
plt.ylabel("Loss")
# plt.legend()
plt.grid(True)
plt.show()

# Visualize Validation Accuracy
plt.figure(figsize=(10, 6))
plt.plot(metrics_tracker.val_accuracies, label="Validation Accuracy")
plt.title("Validation Accuracy Over Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend()
plt.grid(True)
plt.show()

# Function to extract predictions from the model
activity_classes = [
    "brushing_teeth", "idle", "preparing_sandwich", "reading_book",
    "stairs_down", "stairs_up", "typing", "using_phone", "using_remote_control",
    "walking_freely", "walking_holding_a_tray", "walking_with_handbag",
    "walking_with_hands_in_pockets", "walking_with_object_underarm",
    "washing_face_and_hands", "washing_mug", "washing_plate", "writing"
]
def extract_predictions(model, dataloader, activity_classes):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    model.eval()

    all_predictions = []
    all_labels = []
    all_probabilities = []

    with torch.no_grad():
        for batch in dataloader:
            inputs, labels = batch
            inputs = inputs.to(device)
            outputs = model(inputs)

            probabilities = F.softmax(outputs, dim=1).cpu().numpy()
            predicted_classes = np.argmax(probabilities, axis=1)

            all_predictions.extend(predicted_classes)
            all_labels.extend(labels.numpy())
            all_probabilities.extend(probabilities)

    return np.array(all_predictions), np.array(all_labels), np.array(all_probabilities)
# Extract predictions, true labels, and probabilities
predictions, true_labels, probabilities = extract_predictions(model, val_loader, activity_classes)

# High confidence threshold (e.g., >90%)
high_confidence_threshold = 0.9
# Low confidence threshold (e.g., <60%)
low_confidence_threshold = 0.6

# Identify good classifications (correct predictions with high confidence)
good_indices = [
    i for i in range(len(predictions))
    if predictions[i] == true_labels[i] and max(probabilities[i]) >= high_confidence_threshold
]

# Identify bad classifications (incorrect predictions with high confidence)
bad_indices = [
    i for i in range(len(predictions))
    if predictions[i] != true_labels[i] and max(probabilities[i]) >= high_confidence_threshold
]

# Identify uncertain predictions (confidence below threshold)
uncertain_indices = [
    i for i in range(len(predictions))
    if max(probabilities[i]) < low_confidence_threshold
]

# Function to visualize examples with activity names
def plot_example(sequence, true_label_idx, predicted_label_idx, confidence, title, activity_classes, idx):
    true_label = activity_classes[true_label_idx]
    predicted_label = activity_classes[predicted_label_idx]

    plt.figure(figsize=(10, 4))
    for i, axis in enumerate(["X", "Y", "Z"]):
        plt.plot(sequence[:, i], label=f"{axis}-axis")
    plt.title(
        f"{title}\nID: {idx}, True Activity: {true_label}, Predicted: {predicted_label}, Confidence: {confidence:.2f}"
    )
    plt.xlabel("Time Step")
    plt.ylabel("Sensor Value")
    plt.legend()
    plt.grid(True)
    plt.show()

# Visualize good classifications
print("Good Classifications:")
for idx in good_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Good Classification",
        activity_classes,
        idx
    )

# Visualize bad classifications
print("Bad Classifications:")
for idx in bad_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Bad Classification",
        activity_classes,
        idx
    )

# Visualize uncertain predictions
print("Uncertain Predictions:")
for idx in uncertain_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Uncertain Prediction",
        activity_classes,
    idx)

Validation: |          | 0/? [00:00<?, ?it/s]
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃      Validate metric      ┃       DataLoader 0        ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│          val_acc          │    0.5049751400947571     │
│         val_loss          │    1.2855775356292725     │
└───────────────────────────┴───────────────────────────┘

Metrics saved to /kaggle/working/metrics_log.txt
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Good Classifications:
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Bad Classifications:
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Uncertain Predictions:
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LSTM:¶

Pre-processing:¶

In [ ]:
# Function to extract statistical features from a sequence
def extract_statistical_features(sequence):
    features = {}
    for col in ["x", "y", "z"]:
        features[f"{col}_mean"] = sequence[col].mean()
        features[f"{col}_std"] = sequence[col].std()
        features[f"{col}_skew"] = skew(sequence[col])
        features[f"{col}_kurtosis"] = kurtosis(sequence[col])
    return features

# Function to calculate Signal Magnitude Area (SMA)
def calculate_sma(sequence):
    sma = np.sum(np.abs(sequence[["x", "y", "z"]]), axis=1).mean()
    return {"sma": sma}

# Function to calculate zero-crossing rate
def calculate_zero_crossing_rate(sequence):
    zcr = {}
    for col in ["x", "y", "z"]:
        zcr[f"{col}_zcr"] = ((sequence[col][1:].values * sequence[col][:-1].values) < 0).mean()
    return zcr

# Function to process sensor data and unify features
def process_sensor_data(sequence):
    # Extract statistical features
    stats_features = extract_statistical_features(sequence)

    # Calculate SMA
    sma_features = calculate_sma(sequence)

    # Calculate zero-crossing rate
    zcr_features = calculate_zero_crossing_rate(sequence)

    # Combine all features
    features = {**stats_features, **sma_features, **zcr_features}

    # # Add metadata features (e.g., right vs. left, hand vs. foot)
    # for col in ["sensor_smartwatch", "sensor_vicon", "body_part_hand", "body_part_foot", "side_right", "side_left"]:
    #     features[col] = metadata_row[col]

    # Add sequence length as a feature
    # features["sequence_length"] = len(sequence)

    return features

# Main function to process all sequences
def process_sequences(data, filename, id_list, directory):
    if filename.endswith('.csv'):  # Check if the file is a CSV
        file_path = os.path.join(directory, filename)
        file_id = int(filename[:-4])  # Extract the file ID from the filename
        type = 0
        # Process only files where file_id is in train_ids
        if file_id in id_list:
            try:
                # Read the CSV file
                df = pd.read_csv(file_path)

                # Check if 'measurement type' column exists
                if 'measurement type' in df.columns:
                    type = 1
                    # Filter for acceleration data
                    df = df[df['measurement type'] == 'acceleration [m/s/s]']

                    # Add relevant columns based on train DataFrame
                    for col in columns:
                        val = train[train['id'] == file_id][col].values
                        if len(val) > 0:
                            df[col] = val[0]

                    # Drop the 'measurement type' column
                    df.drop(columns=['measurement type'], inplace=True)
                else:
                    type = 2
                    # Rename columns for location data
                    df.rename(columns={
                        'x [m]': 'x',
                        'y [m]': 'y',
                        'z [m]': 'z'
                    }, inplace=True)

                    # Add relevant columns based on train DataFrame
                    for col in columns:
                        val = train[train['id'] == file_id][col].values
                        if len(val) > 0:
                            df[col] = val[0]

            except Exception as e:
                print(f"Error processing file {file_path}: {e}")
                return None

    # Process the sequence and extract features
    features = process_sensor_data(df)
    for col in features.keys():
        df[col] = features[col]
    # Add the corresponding label
    label = labels_dict[file_id]
    seq = torch.tensor(df.values, dtype=torch.float32).permute(1, 0)

    return seq, label

# Preprocess the data directory
directory = '/kaggle/temp/data/unlabeled/unlabeled/'
columns = ['sensor_smartwatch', 'sensor_vicon', 'body_part_hand', 'body_part_foot', 'side_right', 'side_left']


# Extract unique user IDs
user_ids = train['userid'].unique()

# Split user IDs into training and validation sets
train_user_ids, val_user_ids = train_test_split(
    user_ids, test_size=0.2, random_state=42  # Adjust the split ratio as needed
)

# Create training and validation sets based on user IDs
val_user_ids = [4, 5]

# Split the dataset
train_set = train[~train['userid'].isin(val_user_ids)]  # Users not in test_user_ids
val_set = train[train['userid'].isin(val_user_ids)]   # Users in test_user_ids

# Extract IDs for sequences
train_ids = list(train_set['id'])
test_ids = list(val_set['id'])
In [ ]:
train_set = train_set.drop(columns=['userid'])
val_set = val_set.drop(columns=['userid'])
In [ ]:
def pad_to_length(sequence, target_length, padding_value=0.0):
        """
        Pads a sequence to a specific target length.
        Args:
            sequence (Tensor): The input tensor sequence of shape (sequence_length, features).
            target_length (int): The target length to pad the sequence to.
            padding_value (float): The value to pad with (default is 0.0).
        Returns:
            Tensor: The padded sequence.
        """
        seq_length = sequence.size(1)
        if seq_length < target_length:
            # Calculate padding
            padding_needed = target_length - seq_length
            # Pad at the end of the sequence (padding_value is the value to use)
            padded_sequence = F.pad(sequence, (0, padding_needed), value=padding_value)
        else:
            # If the sequence is already long enough, no padding is needed
            padded_sequence = sequence[:target_length]  # Optionally truncate if it's too long
        return padded_sequence

Dataset & Dataloader creations:¶

In [ ]:
class ProcessedSequenceDataset(Dataset):
    def __init__(self, data_dir, labels, id_list, max_len, padding_value=0.0):
        """
        Dataset that preprocesses features (scaling and padding) in the initializer.
        Args:
        - features: List of DataFrames (one per sequence).
        - labels: List of corresponding labels for the sequences.
        - padding_value: Value to use for padding.
        """
        #convert features to tensors
        self.files = [f'{ID}.csv' for ID in id_list]
        self.labels = labels
        self.padding_value = padding_value
        self.id_list = id_list
        self.data_dir = data_dir
        self.max_len = max_len

    def __len__(self):
        return len(self.files)


    def __getitem__(self, idx):
        """
        Get a single sequence and its label.
        """
        filename = self.files[idx]
        seq, label =  process_sequences(train, filename, self.id_list, self.data_dir)
        pad_seq = pad_to_length(seq, self.max_len, self.padding_value)
        return pad_seq, label

    def collate_fn(batch):
        sequences, labels = zip(*batch)

        # Pad sequences along the time dimension (dim=0)
        padded_sequences = pad_sequence(sequences, batch_first=True)

        labels = torch.tensor(labels)
        res = (padded_sequences, labels)
        return res
In [ ]:
data_dir = '/kaggle/temp/data/unlabeled/unlabeled/'

train_dataset = ProcessedSequenceDataset(data_dir,labels_dict, train_ids,max_len)
val_dataset = ProcessedSequenceDataset(data_dir,labels_dict,val_ids, max_len)
batch_size = 128

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=ProcessedSequenceDataset.collate_fn, num_workers=4)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, collate_fn=ProcessedSequenceDataset.collate_fn, num_workers=4)

Summary of the LSTM Model Features¶

Model Architecture¶

  1. Layer Design:

    • Multi-layer LSTM model with:
      • Input layer matching the number of features (e.g., x, y, z axes).
      • Hidden layers of size 128 units (default).
      • There are 3 hidden layers in the model.
      • Final fully connected (FC) layer for classification.

  2. Batch Normalization:

    • Batch normalization applied after each LSTM layer for improved convergence and generalization.

  3. Bidirectional LSTM (Optional):

    • Can process input sequences in both forward and reverse directions for richer context.

  4. Output Layer:

    • Fully connected layer with the number of output neurons equal to the number of classes.

Training and Optimization¶

  1. Loss Function:

    • Cross-Entropy Loss for multi-class classification.

  2. Optimizer:

    • Adam optimizer with a learning rate of 0.0005.

  3. Metrics:

    • Tracks training and validation loss (train_loss and val_loss).
    • Validation accuracy (val_acc) logged at each epoch.

  4. Early Stopping:

    • Monitors val_loss with a patience of 5 epochs to prevent overfitting.

  5. Model Checkpointing:

    • Saves the best model based on minimal val_loss.

Data Handling¶

  1. Input Features:

    • Sequence data preprocessed to match the maximum sequence length across the dataset.
    • Padded sequences to ensure uniform input shape.

  2. Batching:

    • Utilizes PyTorch DataLoader for efficient batching.

  3. Collate Function:

    • Custom collate function ensures proper padding of sequences within batches.

Logging and Monitoring¶

  1. MLFlow Integration:

    • Logs hyperparameters (e.g., input_dim, hidden_dim, learning_rate).
    • Tracks metrics such as loss and accuracy for training and validation.
    • Stores the best model and performance logs for analysis.

  2. Metrics Tracking:

    • Custom callback class to record and save training/validation losses and accuracies for further visualization.

Key Features¶

  • Flexible Design:
    • Allows parameterization of hidden dimensions, layers, and bidirectionality.
  • Regularization:
    • Batch normalization for robust training.
  • Early Stopping and Checkpoints:
    • Prevents overfitting and ensures the best model is retained.
  • Integrated Logging:
    • Seamless integration with MLFlow for experiment tracking and reproducibility.
In [ ]:
class MetricsTracker(Callback):
    def __init__(self):
        self.train_losses = []
        self.val_losses = []
        self.val_accuracies = []

    def on_train_epoch_end(self, trainer, pl_module):
        # Get the training loss from the logged metrics
        train_loss = trainer.callback_metrics.get("train_loss")
        if train_loss:
            self.train_losses.append(train_loss.item())

    def on_validation_epoch_end(self, trainer, pl_module):
        # Get the validation loss and accuracy from the logged metrics
        val_loss = trainer.callback_metrics.get("val_loss")
        val_acc = trainer.callback_metrics.get("val_acc")
        if val_loss:
            self.val_losses.append(val_loss.item())
        if val_acc:
            self.val_accuracies.append(val_acc.item())

    def save_metrics_to_file(self, file_path):
        # Save metrics to a text file
        with open(file_path, "w") as f:
            f.write("Training and Validation Metrics\n")
            f.write("=" * 40 + "\n")
            f.write("Epoch, Train Loss, Validation Loss, Validation Accuracy\n")
            for i in range(len(self.train_losses)):
                train_loss = self.train_losses[i] if i < len(self.train_losses) else "N/A"
                val_loss = self.val_losses[i] if i < len(self.val_losses) else "N/A"
                val_acc = self.val_accuracies[i] if i < len(self.val_accuracies) else "N/A"
                f.write(f"{i+1}, {train_loss}, {val_loss}, {val_acc}\n")
        print(f"Metrics saved to {file_path}")

# Setup MLflow
mlflow.set_experiment("Simple_LSTM_Experiment")

# MLflow Logger
mlflow_logger = MLFlowLogger(
    experiment_name="Simple_LSTM_Experiment",
    run_name="LSTM_with_BatchNorm"
)



# LSTM Model with Batch Normalization
class SimpleLSTM(pl.LightningModule):
    def __init__(self, input_dim, hidden_dim, num_classes, num_layers=3, bidirectional=True, learning_rate=0.001):
        super(SimpleLSTM, self).__init__()
        self.save_hyperparameters()

        self.lstm_layers = nn.ModuleList()
        self.batch_norm_layers = nn.ModuleList()

        for i in range(num_layers):
            input_size = input_dim if i == 0 else hidden_dim * (2 if bidirectional else 1)
            lstm_layer = nn.LSTM(
                input_size=input_size,
                hidden_size=hidden_dim,
                num_layers=1,
                bidirectional=bidirectional,
                batch_first=True
            )
            self.lstm_layers.append(lstm_layer)
            self.batch_norm_layers.append(
                nn.BatchNorm1d(hidden_dim * (2 if bidirectional else 1))
            )

        self.fc = nn.Linear(hidden_dim * (2 if bidirectional else 1), num_classes)
        self.learning_rate = learning_rate

    def forward(self, x):
        for lstm_layer, batch_norm_layer in zip(self.lstm_layers, self.batch_norm_layers):
            x, _ = lstm_layer(x)
            x = x.permute(0, 2, 1)  # Switch to (batch, features, sequence_length)
            x = batch_norm_layer(x)
            x = x.permute(0, 2, 1)  # Switch back to (batch, sequence_length, features)

        x = x[:, -1, :]  # Last time step
        output = self.fc(x)
        return output

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = nn.CrossEntropyLoss()(y_hat, y)
        self.log("train_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
        return loss

    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = nn.CrossEntropyLoss()(y_hat, y)
        acc = (torch.argmax(y_hat, dim=1) == y).float().mean()
        self.log("val_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
        self.log("val_acc", acc, on_step=False, on_epoch=True, prog_bar=True, logger=True)

    def configure_optimizers(self):
        return optim.Adam(self.parameters(), lr=self.learning_rate)

# Define input dimensions and parameters
max_len = train_loader.dataset.max_len  # Adjust based on dataset
input_dim = max_len  # Number of features (e.g., x, y, z axes)
hidden_dim = 128
num_classes = len(set(labels_dict.values()))  # Number of unique labels
learning_rate = 0.0005

# Initialize Model
model = SimpleLSTM(
    input_dim=input_dim,
    hidden_dim=hidden_dim,
    num_classes=num_classes,
    num_layers=3,
    bidirectional=False,
    learning_rate=learning_rate
)

# Trainer setup
# Initialize the metrics tracker
metrics_tracker = MetricsTracker()

# Trainer setup
trainer = pl.Trainer(
    max_epochs=10,
    precision=16,
    logger=mlflow_logger,
    callbacks=[
        EarlyStopping(monitor='val_loss', patience=5, mode='min'),
        ModelCheckpoint(monitor='val_loss', mode='min', save_top_k=1, filename='best_model'),
        metrics_tracker  # Add metrics tracker callback here
    ],
    accelerator="gpu" if torch.cuda.is_available() else "cpu",
    devices=torch.cuda.device_count()
)


# Train and Validate the Model
with mlflow.start_run():
    # Log parameters to MLflow
    mlflow.log_param("input_dim", input_dim)
    mlflow.log_param("hidden_dim", hidden_dim)
    mlflow.log_param("num_layers", 3)
    mlflow.log_param("bidirectional", False)
    mlflow.log_param("learning_rate", learning_rate)
    mlflow.log_param("max_len", max_len)

    # Train the model
    trainer.fit(model, train_loader, val_loader)

    # Validate the model
    val_results = trainer.validate(model, val_loader)

    # Log validation results
    for metric in val_results:
        for k, v in metric.items():
            mlflow.log_metric(k, v)

# Save metrics to file
metrics_tracker.save_metrics_to_file("/kaggle/working/metrics_log.txt")

/usr/local/lib/python3.10/dist-packages/lightning_fabric/connector.py:571: `precision=16` is supported for historical reasons but its usage is discouraged. Please set your precision to 16-mixed instead!

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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃      Validate metric      ┃       DataLoader 0        ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│          val_acc          │    0.8070548176765442     │
│         val_loss          │    0.5215221047401428     │
└───────────────────────────┴───────────────────────────┘

Metrics saved to /kaggle/working/metrics_log.txt

Visualizations¶

Plot training and validation loss¶

In [ ]:
train_losses = metrics_tracker.train_losses
validation_losses = metrics_tracker.val_losses

# Pad train_losses to match the length of validation_losses
if len(train_losses) < len(validation_losses):
    padding_value = train_losses[-1] if train_losses else 0  # Use last value or 0 if empty
    train_losses += [padding_value] * (len(validation_losses) - len(train_losses))

metrics_tracker.train_losses = train_losses
# Visualize Training and Validation Loss
plt.figure(figsize=(10, 6))
plt.plot(metrics_tracker.train_losses, label="Train Loss")
plt.plot(metrics_tracker.val_losses, label="Validation Loss")
plt.title("Loss Over Epochs")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.show()

# Visualize Validation Accuracy
plt.figure(figsize=(10, 6))
plt.plot(metrics_tracker.val_accuracies, label="Validation Accuracy")
plt.title("Validation Accuracy Over Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend()
plt.grid(True)
plt.show()
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In [ ]:
# Function to extract predictions from the model
activity_classes = [
    "brushing_teeth", "idle", "preparing_sandwich", "reading_book",
    "stairs_down", "stairs_up", "typing", "using_phone", "using_remote_control",
    "walking_freely", "walking_holding_a_tray", "walking_with_handbag",
    "walking_with_hands_in_pockets", "walking_with_object_underarm",
    "washing_face_and_hands", "washing_mug", "washing_plate", "writing"
]
def extract_predictions(model, dataloader, activity_classes):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    model.eval()

    all_predictions = []
    all_labels = []
    all_probabilities = []

    with torch.no_grad():
        for batch in dataloader:
            inputs, labels = batch
            inputs = inputs.to(device)
            outputs = model(inputs)

            probabilities = F.softmax(outputs, dim=1).cpu().numpy()
            predicted_classes = np.argmax(probabilities, axis=1)

            all_predictions.extend(predicted_classes)
            all_labels.extend(labels.numpy())
            all_probabilities.extend(probabilities)

    return np.array(all_predictions), np.array(all_labels), np.array(all_probabilities)
# Extract predictions, true labels, and probabilities
predictions, true_labels, probabilities = extract_predictions(model, val_loader, activity_classes)

# High confidence threshold (e.g., >90%)
high_confidence_threshold = 0.9
# Low confidence threshold (e.g., <60%)
low_confidence_threshold = 0.6

# Identify good classifications (correct predictions with high confidence)
good_indices = [
    i for i in range(len(predictions))
    if predictions[i] == true_labels[i] and max(probabilities[i]) >= high_confidence_threshold
]

# Identify bad classifications (incorrect predictions with high confidence)
bad_indices = [
    i for i in range(len(predictions))
    if predictions[i] != true_labels[i] and max(probabilities[i]) >= high_confidence_threshold
]

# Identify uncertain predictions (confidence below threshold)
uncertain_indices = [
    i for i in range(len(predictions))
    if max(probabilities[i]) < low_confidence_threshold
]
In [ ]:
# Function to visualize examples with activity names
def plot_example(sequence, true_label_idx, predicted_label_idx, confidence, title, activity_classes, idx):
    true_label = activity_classes[true_label_idx]
    predicted_label = activity_classes[predicted_label_idx]

    plt.figure(figsize=(10, 4))
    for i, axis in enumerate(["X", "Y", "Z"]):
        plt.plot(sequence[:, i], label=f"{axis}-axis")
    plt.title(
        f"{title}\nID: {idx}, True Activity: {true_label}, Predicted: {predicted_label}, Confidence: {confidence:.2f}"
    )
    plt.xlabel("Time Step")
    plt.ylabel("Sensor Value")
    plt.legend()
    plt.grid(True)
    plt.show()

# Visualize good classifications
print("Good Classifications:")
for idx in good_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Good Classification",
        activity_classes,
        idx
    )

# Visualize bad classifications
print("Bad Classifications:")
for idx in bad_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Bad Classification",
        activity_classes,
        idx
    )

# Visualize uncertain predictions
print("Uncertain Predictions:")
for idx in uncertain_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Uncertain Prediction",
        activity_classes,
        idx
    )

Good Classifications:
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Bad Classifications:
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Uncertain Predictions:
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In [ ]:
# Compute the confusion matrix
conf_matrix = confusion_matrix(true_labels, predictions)

# Get label names from the label encoder
label_names = label_encoder.classes_

# Plot the confusion matrix
plt.figure(figsize=(10, 8))
sns.heatmap(conf_matrix, annot=True, fmt="d", cmap="Blues", xticklabels=label_names, yticklabels=label_names)
plt.xlabel("Predicted Label")
plt.ylabel("Actual Label")
plt.title("Confusion Matrix")
plt.show()
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Analysis of Results and Improvement Suggestions¶

f. Analysis of Results¶

LSTM Model¶

1. Performance on Validation Data¶

  • Validation Accuracy: 81%
  • Validation Loss: 0.52
  • These metrics indicate that the model generalizes well on the validation set with minimal overfitting.

2. Key Observations¶

  • Good Classifications:

    • High confidence for activities with distinct sensor patterns, such as:
      • walking_with_hands_in_pockets
      • reading_book
    • These activities likely have unique movement patterns that the model captures well.

  • Bad Classifications:

    • Misclassification between similar activities:
      • washing_plate predicted as washing_mug
      • walking_with_object_underarm predicted as walking_with_handbag
    • This reflects overlapping sensor data for visually and functionally similar activities.

  • Uncertain Predictions:

    • Activities with lower confidence, such as:
      • walking_holding_a_tray predicted as walking_freely
      • These may have high variability or insufficient representation in the training data.

  • Class Imbalance:

    • Activities like stairs_up and stairs_down have fewer samples, leading to poor model performance on these classes.
    • Overrepresented classes like walking_freely dominate the model's learning process.

  • Model Architecture Considerations:

    • LSTM Layers:
      • The architecture uses 3 LSTM layers to capture temporal dependencies in the time-series data.
    • Batch Normalization:
      • While batch normalization stabilizes training, it may smooth out subtle differences in sensor data, which are critical for distinguishing between similar activities (e.g., washing_plate vs. washing_mug).
    • Bidirectionality:
      • The lack of bidirectional LSTMs limits the model’s ability to capture context from both past and future time steps.
    • Missing Dropout:
      • The absence of dropout layers increases the risk of overfitting.

1D CNN Model¶

1. Performance on Validation Data¶

  • Validation Accuracy: 50.4%
  • Validation Loss: 1.29
  • The lower accuracy suggests that the 1D CNN struggles with capturing complex temporal dependencies.

2. Key Observations¶

  • Good Classifications:

    • Performed well on localized patterns, such as stairs_down.
    • These activities often exhibit repetitive, high-frequency motion patterns.

  • Bad Classifications:

    • Misclassifications in activities requiring long-term temporal understanding:
      • walking_holding_a_tray misclassified as walking_freely.
      • washing_plate misclassified as washing_mug.
    • Indicates the model’s limitations in processing sequences with subtle differences.

  • Model Architecture Considerations:

    • Convolutional Layers:
      • Effective for capturing local temporal patterns but lacks the capacity to model long-term dependencies.
    • Dropout Regularization:
      • Helps reduce overfitting but may limit the model’s ability to learn nuanced activity patterns.

Comparative Analysis: LSTM vs. 1D CNN¶

Feature LSTM 1D CNN
Temporal Dependencies Captures long-term dependencies well. Struggles with long sequences but captures local patterns effectively.
Accuracy Higher on validation (81%). Lower validation accuracy (50.4%).
Loss (Validation) Validation: 0.52 Validation: 1.29.
Generalization Possible overfitting to validation data. Limited overfitting but less accurate overall.
Architectural Complexity Higher (3 LSTM layers). Lower (convolutional layers).

Suggestions for Improvement¶

1. Distinguishing Similar Activities¶

  • Weighted Loss Function:
    • Introduce a custom loss function to penalize misclassifications between similar classes more heavily.
    • For example:
      • Higher penalties for washing_plate vs. washing_mug.
      • Lower penalties for reading_book vs. typing.
  • Feature Augmentation:
    • Engineer additional features that emphasize subtle differences in movement patterns (e.g., acceleration deltas, frequency domain features).

2. Improving Generalization & Avoiding overfitting¶

  • Dropout Layers: - Introduce dropout layers after each LSTM layer to prevent overfitting and improve generalization to unseen users.
  • Bidirectional LSTMs: - Enable bidirectional LSTMs to capture context from both forward and backward time steps, improving the model's ability to differentiate overlapping activities.
  • More Simple Architecture: - Reduce the number of LSTM layers & the number of neurons per layer, to avoid overfitting to validation data

3. Addressing Class Imbalance¶

  • Oversampling or Data Augmentation:
    • Use techniques like SMOTE (Synthetic Minority Oversampling Technique) or augmentation (jittering, scaling) for underrepresented activities like stairs_up and stairs_down.
  • Weighted Sampling:
    • Adjust the DataLoader to oversample minority classes during training.

4. Hybrid Model Approach¶

  • Combine the 1D CNN with LSTM layers:
    • Use CNN layers for extracting local patterns and LSTM layers for modeling long-term dependencies.
  • Introduce attention mechanisms to enhance focus on relevant time steps for distinguishing similar activities.

Implementation Plan¶

Priority 1: Custom Weighted Loss Function¶

  • Implement a custom loss function that applies higher penalties for misclassifications between similar classes.

Priority 2: Addressing overfitting:¶

  • Create a more simple LSTM architecture, and include dropout layers, to prevent possible overfitting.

Priority 3: Address Class Imbalance¶

  • Use weighted sampling in the DataLoader to balance class representation during training.
  • Augment underrepresented classes with transformations like scaling and jittering.

Priority 1: Custom Weighted Loss Function¶

In [ ]:
class MetricsTracker(Callback):
    def __init__(self):
        self.train_losses = []
        self.val_losses = []
        self.val_accuracies = []

    def on_train_epoch_end(self, trainer, pl_module):
        # Get the training loss from the logged metrics
        train_loss = trainer.callback_metrics.get("train_loss")
        if train_loss:
            self.train_losses.append(train_loss.item())

    def on_validation_epoch_end(self, trainer, pl_module):
        # Get the validation loss and accuracy from the logged metrics
        val_loss = trainer.callback_metrics.get("val_loss")
        val_acc = trainer.callback_metrics.get("val_acc")
        if val_loss:
            self.val_losses.append(val_loss.item())
        if val_acc:
            self.val_accuracies.append(val_acc.item())

    def save_metrics_to_file(self, file_path):
        # Save metrics to a text file
        with open(file_path, "w") as f:
            f.write("Training and Validation Metrics\n")
            f.write("=" * 40 + "\n")
            f.write("Epoch, Train Loss, Validation Loss, Validation Accuracy\n")
            for i in range(len(self.train_losses)):
                train_loss = self.train_losses[i] if i < len(self.train_losses) else "N/A"
                val_loss = self.val_losses[i] if i < len(self.val_losses) else "N/A"
                val_acc = self.val_accuracies[i] if i < len(self.val_accuracies) else "N/A"
                f.write(f"{i+1}, {train_loss}, {val_loss}, {val_acc}\n")
        print(f"Metrics saved to {file_path}")

# Setup MLflow
mlflow.set_experiment("Simple_LSTM_Experiment")

# MLflow Logger
mlflow_logger = MLFlowLogger(
    experiment_name="Simple_LSTM_Experiment",
    run_name="LSTM_with_BatchNorm"
)



# LSTM Model with Batch Normalization
class SimpleLSTM(pl.LightningModule):
    def __init__(self, input_dim, hidden_dim, num_classes, num_layers=3, bidirectional=True, learning_rate=0.001):
        super(SimpleLSTM, self).__init__()
        self.save_hyperparameters()

        self.lstm_layers = nn.ModuleList()
        self.batch_norm_layers = nn.ModuleList()

        for i in range(num_layers):
            input_size = input_dim if i == 0 else hidden_dim * (2 if bidirectional else 1)
            lstm_layer = nn.LSTM(
                input_size=input_size,
                hidden_size=hidden_dim,
                num_layers=1,
                bidirectional=bidirectional,
                batch_first=True
            )
            self.lstm_layers.append(lstm_layer)
            self.batch_norm_layers.append(
                nn.BatchNorm1d(hidden_dim * (2 if bidirectional else 1))
            )

        self.fc = nn.Linear(hidden_dim * (2 if bidirectional else 1), num_classes)
        self.learning_rate = learning_rate

    def forward(self, x):
        for lstm_layer, batch_norm_layer in zip(self.lstm_layers, self.batch_norm_layers):
            x, _ = lstm_layer(x)
            x = x.permute(0, 2, 1)  # Switch to (batch, features, sequence_length)
            x = batch_norm_layer(x)
            x = x.permute(0, 2, 1)  # Switch back to (batch, sequence_length, features)

        x = x[:, -1, :]  # Last time step
        output = self.fc(x)
        return output

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = nn.CrossEntropyLoss()(y_hat, y)
        self.log("train_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
        return loss

    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = nn.CrossEntropyLoss()(y_hat, y)
        acc = (torch.argmax(y_hat, dim=1) == y).float().mean()
        self.log("val_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
        self.log("val_acc", acc, on_step=False, on_epoch=True, prog_bar=True, logger=True)

    def configure_optimizers(self):
        return optim.Adam(self.parameters(), lr=self.learning_rate)

# Define input dimensions and parameters
max_len = train_loader.dataset.max_len  # Adjust based on dataset
input_dim = max_len  # Number of features (e.g., x, y, z axes)
hidden_dim = 128
num_classes = len(set(labels_dict.values()))  # Number of unique labels
learning_rate = 0.0005

# Initialize Model
model = SimpleLSTM(
    input_dim=input_dim,
    hidden_dim=hidden_dim,
    num_classes=num_classes,
    num_layers=3,
    bidirectional=False,
    learning_rate=learning_rate
)

# Trainer setup
# Initialize the metrics tracker
metrics_tracker = MetricsTracker()

# Trainer setup
trainer = pl.Trainer(
    max_epochs=10,
    precision=16,
    logger=mlflow_logger,
    callbacks=[
        EarlyStopping(monitor='val_loss', patience=5, mode='min'),
        ModelCheckpoint(monitor='val_loss', mode='min', save_top_k=1, filename='best_model'),
        metrics_tracker  # Add metrics tracker callback here
    ],
    accelerator="gpu" if torch.cuda.is_available() else "cpu",
    devices=torch.cuda.device_count()
)


# Train and Validate the Model
with mlflow.start_run():
    # Log parameters to MLflow
    mlflow.log_param("input_dim", input_dim)
    mlflow.log_param("hidden_dim", hidden_dim)
    mlflow.log_param("num_layers", 3)
    mlflow.log_param("bidirectional", False)
    mlflow.log_param("learning_rate", learning_rate)
    mlflow.log_param("max_len", max_len)

    # Train the model
    trainer.fit(model, train_loader, val_loader)

    # Validate the model
    val_results = trainer.validate(model, val_loader)

    # Log validation results
    for metric in val_results:
        for k, v in metric.items():
            mlflow.log_metric(k, v)

# Save metrics to file
metrics_tracker.save_metrics_to_file("/kaggle/working/metrics_log.txt")

/opt/conda/lib/python3.10/site-packages/lightning_fabric/connector.py:571: `precision=16` is supported for historical reasons but its usage is discouraged. Please set your precision to 16-mixed instead!

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┃      Validate metric      ┃       DataLoader 0        ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│          val_acc          │    0.3320380747318268     │
│         val_loss          │     2.315869092941284     │
└───────────────────────────┴───────────────────────────┘

Metrics saved to /kaggle/working/metrics_log.txt

Visualizations¶

Plot training and validation loss¶

In [ ]:
train_losses = metrics_tracker.train_losses
validation_losses = metrics_tracker.val_losses

# Pad train_losses to match the length of validation_losses
if len(train_losses) < len(validation_losses):
    padding_value = train_losses[-1] if train_losses else 0  # Use last value or 0 if empty
    train_losses += [padding_value] * (len(validation_losses) - len(train_losses))

metrics_tracker.train_losses = train_losses
# Visualize Training and Validation Loss
plt.figure(figsize=(10, 6))
plt.plot(metrics_tracker.train_losses, label="Train Loss")
plt.plot(metrics_tracker.val_losses, label="Validation Loss")
plt.title("Loss Over Epochs")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.show()

# Visualize Validation Accuracy
plt.figure(figsize=(10, 6))
plt.plot(metrics_tracker.val_accuracies, label="Validation Accuracy")
plt.title("Validation Accuracy Over Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend()
plt.grid(True)
plt.show()
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Classifications example:¶

In [ ]:
# Function to extract predictions from the model
activity_classes = [
    "brushing_teeth", "idle", "preparing_sandwich", "reading_book",
    "stairs_down", "stairs_up", "typing", "using_phone", "using_remote_control",
    "walking_freely", "walking_holding_a_tray", "walking_with_handbag",
    "walking_with_hands_in_pockets", "walking_with_object_underarm",
    "washing_face_and_hands", "washing_mug", "washing_plate", "writing"
]
def extract_predictions(model, dataloader, activity_classes):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    model.eval()

    all_predictions = []
    all_labels = []
    all_probabilities = []

    with torch.no_grad():
        for batch in dataloader:
            inputs, labels = batch
            inputs = inputs.to(device)
            outputs = model(inputs)

            probabilities = F.softmax(outputs, dim=1).cpu().numpy()
            predicted_classes = np.argmax(probabilities, axis=1)

            all_predictions.extend(predicted_classes)
            all_labels.extend(labels.numpy())
            all_probabilities.extend(probabilities)

    return np.array(all_predictions), np.array(all_labels), np.array(all_probabilities)
# Extract predictions, true labels, and probabilities
predictions, true_labels, probabilities = extract_predictions(model, val_loader, activity_classes)

# High confidence threshold (e.g., >90%)
high_confidence_threshold = 0.9
# Low confidence threshold (e.g., <60%)
low_confidence_threshold = 0.6

# Identify good classifications (correct predictions with high confidence)
good_indices = [
    i for i in range(len(predictions))
    if predictions[i] == true_labels[i] and max(probabilities[i]) >= high_confidence_threshold
]

# Identify bad classifications (incorrect predictions with high confidence)
bad_indices = [
    i for i in range(len(predictions))
    if predictions[i] != true_labels[i] and max(probabilities[i]) >= high_confidence_threshold
]

# Identify uncertain predictions (confidence below threshold)
uncertain_indices = [
    i for i in range(len(predictions))
    if max(probabilities[i]) < low_confidence_threshold
]


# Function to visualize examples with activity names
def plot_example(sequence, true_label_idx, predicted_label_idx, confidence, title, activity_classes, idx):
    true_label = activity_classes[true_label_idx]
    predicted_label = activity_classes[predicted_label_idx]

    plt.figure(figsize=(10, 4))
    for i, axis in enumerate(["X", "Y", "Z"]):
        plt.plot(sequence[:, i], label=f"{axis}-axis")
    plt.title(
        f"{title}\nID: {idx}, True Activity: {true_label}, Predicted: {predicted_label}, Confidence: {confidence:.2f}"
    )
    plt.xlabel("Time Step")
    plt.ylabel("Sensor Value")
    plt.legend()
    plt.grid(True)
    plt.show()

# Visualize good classifications
print("Good Classifications:")
for idx in good_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Good Classification",
        activity_classes,
        idx
    )

# Visualize bad classifications
print("Bad Classifications:")
for idx in bad_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Bad Classification",
        activity_classes,
        idx
    )

# Visualize uncertain predictions
print("Uncertain Predictions:")
for idx in uncertain_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Uncertain Prediction",
        activity_classes,
        idx
    )

Good Classifications:
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Bad Classifications:
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Uncertain Predictions:
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Priority 2: Addressing overfitting:¶

In [ ]:
class MetricsTracker(Callback):
    def __init__(self):
        self.train_losses = []
        self.val_losses = []
        self.val_accuracies = []

    def on_train_epoch_end(self, trainer, pl_module):
        # Get the training loss from the logged metrics
        train_loss = trainer.callback_metrics.get("train_loss")
        if train_loss:
            self.train_losses.append(train_loss.item())

    def on_validation_epoch_end(self, trainer, pl_module):
        # Get the validation loss and accuracy from the logged metrics
        val_loss = trainer.callback_metrics.get("val_loss")
        val_acc = trainer.callback_metrics.get("val_acc")
        if val_loss:
            self.val_losses.append(val_loss.item())
        if val_acc:
            self.val_accuracies.append(val_acc.item())

    def save_metrics_to_file(self, file_path):
        # Save metrics to a text file
        with open(file_path, "w") as f:
            f.write("Training and Validation Metrics\n")
            f.write("=" * 40 + "\n")
            f.write("Epoch, Train Loss, Validation Loss, Validation Accuracy\n")
            for i in range(len(self.train_losses)):
                train_loss = self.train_losses[i] if i < len(self.train_losses) else "N/A"
                val_loss = self.val_losses[i] if i < len(self.val_losses) else "N/A"
                val_acc = self.val_accuracies[i] if i < len(self.val_accuracies) else "N/A"
                f.write(f"{i+1}, {train_loss}, {val_loss}, {val_acc}\n")
        print(f"Metrics saved to {file_path}")

# Setup MLflow
mlflow.set_experiment("Simple_LSTM_Experiment")

# MLflow Logger
mlflow_logger = MLFlowLogger(
    experiment_name="Simple_LSTM_Experiment",
    run_name="LSTM_with_BatchNorm"
)



# LSTM Model with Batch Normalization
class SimpleLSTM(pl.LightningModule):
    def __init__(self, input_dim, hidden_dims, num_classes, learning_rate=0.001, dropout_rate=0.3):
        """
        Simplified LSTM model with two layers and dropout.
        Args:
            input_dim (int): Input dimension (number of features).
            hidden_dims (list): List of hidden dimensions for LSTM layers (e.g., [128, 64]).
            num_classes (int): Number of output classes.
            learning_rate (float): Learning rate for the optimizer.
            dropout_rate (float): Dropout rate to prevent overfitting.
        """
        super(SimpleLSTM, self).__init__()
        self.save_hyperparameters()

        # First LSTM layer
        self.lstm1 = nn.LSTM(
            input_size=input_dim,
            hidden_size=hidden_dims[0],
            num_layers=1,
            batch_first=True
        )

        # Dropout between layers
        self.dropout = nn.Dropout(dropout_rate)

        # Second LSTM layer
        self.lstm2 = nn.LSTM(
            input_size=hidden_dims[0],
            hidden_size=hidden_dims[1],
            num_layers=1,
            batch_first=True
        )

        # Fully connected output layer
        self.fc = nn.Linear(hidden_dims[1], num_classes)

        # Store the learning rate
        self.learning_rate = learning_rate

    def forward(self, x):
        """
        Forward pass of the LSTM model.
        """
        x, _ = self.lstm1(x)  # Pass through the first LSTM layer
        x = self.dropout(x)   # Apply dropout
        x, _ = self.lstm2(x)  # Pass through the second LSTM layer

        x = x[:, -1, :]  # Take the output from the last time step
        output = self.fc(x)  # Fully connected layer for predictions
        return output

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = nn.CrossEntropyLoss()(y_hat, y)
        self.log("train_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
        return loss

    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = nn.CrossEntropyLoss()(y_hat, y)
        acc = (torch.argmax(y_hat, dim=1) == y).float().mean()
        self.log("val_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
        self.log("val_acc", acc, on_step=False, on_epoch=True, prog_bar=True, logger=True)

    def configure_optimizers(self):
        return optim.Adam(self.parameters(), lr=self.learning_rate)


# Define input dimensions and parameters
max_len = train_loader.dataset.max_len  # Adjust based on dataset
input_dim = max_len  # Number of features (e.g., x, y, z axes)
num_classes = len(set(labels_dict.values()))  # Number of unique labels
learning_rate = 0.0005

# Initialize the simplified LSTM model
hidden_dims = [128, 64]  # Hidden dimensions for LSTM layers
dropout_rate = 0.3       # Dropout rate to prevent overfitting

model = SimpleLSTM(
    input_dim=max_len,       # Number of input features (e.g., x, y, z)
    hidden_dims=hidden_dims, # Hidden dimensions [128, 64]
    num_classes=len(set(labels_dict.values())), # Number of classes
    learning_rate=0.0005,    # Learning rate
    dropout_rate=dropout_rate
)


# Trainer setup
# Initialize the metrics tracker
metrics_tracker = MetricsTracker()

# Trainer setup
trainer = pl.Trainer(
    max_epochs=10,
    precision=16,
    logger=mlflow_logger,
    callbacks=[
        EarlyStopping(monitor='val_loss', patience=5, mode='min'),
        ModelCheckpoint(monitor='val_loss', mode='min', save_top_k=1, filename='best_model'),
        metrics_tracker  # Add metrics tracker callback here
    ],
    accelerator="gpu" if torch.cuda.is_available() else "cpu",
    devices=torch.cuda.device_count()
)


# Train and Validate the Model
with mlflow.start_run():
    # Log parameters to MLflow
    mlflow.log_param("input_dim", input_dim)
    mlflow.log_param("hidden_dims", hidden_dims)
    mlflow.log_param("num_layers", 3)
    mlflow.log_param("bidirectional", False)
    mlflow.log_param("learning_rate", learning_rate)
    mlflow.log_param("max_len", max_len)

    # Train the model
    trainer.fit(model, train_loader, val_loader)

    # Validate the model
    val_results = trainer.validate(model, val_loader)

    # Log validation results
    for metric in val_results:
        for k, v in metric.items():
            mlflow.log_metric(k, v)

# Save metrics to file
metrics_tracker.save_metrics_to_file("/kaggle/working/metrics_log.txt")

/usr/local/lib/python3.10/dist-packages/lightning_fabric/connector.py:571: `precision=16` is supported for historical reasons but its usage is discouraged. Please set your precision to 16-mixed instead!

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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃      Validate metric      ┃       DataLoader 0        ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│          val_acc          │    0.34076130390167236    │
│         val_loss          │    1.8614206314086914     │
└───────────────────────────┴───────────────────────────┘

Metrics saved to /kaggle/working/metrics_log.txt

Visualizations¶

Plot training and validation loss¶

In [ ]:
train_losses = metrics_tracker.train_losses
validation_losses = metrics_tracker.val_losses

# Pad train_losses to match the length of validation_losses
if len(train_losses) < len(validation_losses):
    padding_value = train_losses[-1] if train_losses else 0  # Use last value or 0 if empty
    train_losses += [padding_value] * (len(validation_losses) - len(train_losses))

metrics_tracker.train_losses = train_losses
# Visualize Training and Validation Loss
plt.figure(figsize=(10, 6))
plt.plot(metrics_tracker.train_losses, label="Train Loss")
plt.plot(metrics_tracker.val_losses, label="Validation Loss")
plt.title("Loss Over Epochs")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.legend()
plt.grid(True)
plt.show()

# Visualize Validation Accuracy
plt.figure(figsize=(10, 6))
plt.plot(metrics_tracker.val_accuracies, label="Validation Accuracy")
plt.title("Validation Accuracy Over Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend()
plt.grid(True)
plt.show()
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Classifications Examples:¶

In [ ]:
# Function to visualize examples with activity names
def plot_example(sequence, true_label_idx, predicted_label_idx, confidence, title, activity_classes, idx):
    true_label = activity_classes[true_label_idx]
    predicted_label = activity_classes[predicted_label_idx]

    plt.figure(figsize=(10, 4))
    for i, axis in enumerate(["X", "Y", "Z"]):
        plt.plot(sequence[:, i], label=f"{axis}-axis")
    plt.title(
        f"{title}\nID: {idx}, True Activity: {true_label}, Predicted: {predicted_label}, Confidence: {confidence:.2f}"
    )
    plt.xlabel("Time Step")
    plt.ylabel("Sensor Value")
    plt.legend()
    plt.grid(True)
    plt.show()

# Visualize good classifications
print("Good Classifications:")
for idx in good_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Good Classification",
        activity_classes,
        idx
    )

# Visualize bad classifications
print("Bad Classifications:")
for idx in bad_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Bad Classification",
        activity_classes,
        idx
    )

# Visualize uncertain predictions
print("Uncertain Predictions:")
for idx in uncertain_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Uncertain Prediction",
        activity_classes,
        idx
    )

Good Classifications:
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Bad Classifications:
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Uncertain Predictions:
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Analysis of Results for LSTM Priorities¶

Priority 1: Custom Weighted Loss Function¶

  1. Impact on Performance:

    • The implementation of a weighted loss function introduced penalties for misclassifications between similar activity classes.
    • Validation accuracy decreased significantly to 33.2%, and validation loss increased to 2.31, highlighting the need for better weight tuning.
    • Overall performance declined due to imbalance in penalty scaling.

  2. Drawbacks:

    • Weighted loss resulted in unstable learning and optimization issues, likely due to aggressive penalization for certain class pairs.
    • Training time increased marginally due to the computation of class weights.

Priority 2: Simplified LSTM Architecture¶

  1. Impact on Performance:

    • Reducing LSTM layers from 3 to 2 and adding dropout layers improved regularization.
    • Validation accuracy increased slightly to 34.0%, while validation loss decreased to 1.86, compared to Priority 1. However, overall accuracy remained low, indicating room for further tuning.
    • The reduced architecture helped in stabilizing optimization compared to the weighted loss implementation.

  2. Drawbacks:

    • Although dropout reduced overfitting, it also led to underutilization of model capacity, limiting the ability to capture complex patterns.
    • Performance on minority classes did not improve significantly, indicating that underrepresentation remains a challenge.

Results Table¶

Model Variant Validation Accuracy (%) Validation Loss Notes
Baseline LSTM 81.0 0.52 Original architecture with 3 layers, no dropout, and uniform loss. Struggled with underrepresented and overlapping activity classes.
Priority 1: Weighted Loss 33.2 2.31 Weighted loss function destabilized optimization, leading to reduced accuracy and higher loss. Over-penalization for certain class misclassifications.
Priority 2: Simplified LSTM 34.0 1.86 Simplified architecture with 2 LSTMs and dropout layers improved generalization but showed limited ability to capture complex activity patterns.

Where the Model is Doing Worse¶

  1. Underrepresented Classes:

    • Both Priority 1 and 2 fail to address misclassifications for minority activity classes due to the dataset's class imbalance.
    • Activities with fewer samples (e.g., cleaning_table, dusting) are consistently misclassified into similar but majority classes.

  2. Overlapping Classes:

    • While weighted loss attempted to tackle overlapping classes, results remained suboptimal, particularly for activities such as washing_plate vs. washing_mug.

  3. Generalization Across Classes:

    • The simplified architecture, while reducing overfitting, was unable to generalize effectively across all activity types, as seen in the low overall validation accuracy.

Future Directions¶

  1. Data Augmentation:

    • Implement techniques such as synthetic data generation, time-series jittering, and window slicing to address underrepresented classes and balance the dataset.

  2. Attention Mechanisms:

    • Integrate attention layers into the LSTM model to dynamically focus on critical features and time steps, improving performance for overlapping classes.

  3. Hyperparameter Tuning:

    • Explore grid or random search for optimizing dropout rates, LSTM units, and learning rates to achieve a better balance between underfitting and overfitting.

Key Observations¶

  • Weighted loss, although promising, destabilized the training process due to aggressive penalties.
  • Simplified architecture improved stability but failed to capture complex patterns, resulting in subpar performance across the board.
  • Addressing dataset imbalance and incorporating advanced architectural elements (e.g., attention) are critical for future improvements.

Test the LSTM model:¶

In [ ]:
# Preprocess the test directory
directory = '/kaggle/temp/data/unlabeled/unlabeled/'
columns = ['sensor_smartwatch', 'sensor_vicon', 'body_part_hand', 'body_part_foot', 'side_right', 'side_left']
test_dfs = {}
metadata_path = '/kaggle/input/dlw2test/metadata.csv'  # Path to test metadata
test_metadata = pd.read_csv(metadata_path)
test_metadata = pd.get_dummies(test_metadata, columns=['sensor', 'body_part', 'side'])
for col in ['sensor_smartwatch','sensor_vicon', 'body_part_hand','body_part_foot', 'side_right', 'side_left']:
    test_metadata[col] = test_metadata[col].astype(int)
test_metadata.rename(columns={"sample_id": "id"}, inplace=True)

test_metadata=test_metadata.drop(columns=['userid'])
user_test_ids = test_metadata['id'].values


# Function to extract statistical features from a sequence
def extract_statistical_features(sequence):
    features = {}
    for col in ["x", "y", "z"]:
        features[f"{col}_mean"] = sequence[col].mean()
        features[f"{col}_std"] = sequence[col].std()
        features[f"{col}_skew"] = skew(sequence[col])
        features[f"{col}_kurtosis"] = kurtosis(sequence[col])
    return features

# Function to calculate Signal Magnitude Area (SMA)
def calculate_sma(sequence):
    sma = np.sum(np.abs(sequence[["x", "y", "z"]]), axis=1).mean()
    return {"sma": sma}

# Function to calculate zero-crossing rate
def calculate_zero_crossing_rate(sequence):
    zcr = {}
    for col in ["x", "y", "z"]:
        zcr[f"{col}_zcr"] = ((sequence[col][1:].values * sequence[col][:-1].values) < 0).mean()
    return zcr

# Function to process sensor data and unify features
def process_sensor_data(sequence):
    # Extract statistical features
    stats_features = extract_statistical_features(sequence)

    # Calculate SMA
    sma_features = calculate_sma(sequence)

    # Calculate zero-crossing rate
    zcr_features = calculate_zero_crossing_rate(sequence)

    # Combine all features
    features = {**stats_features, **sma_features, **zcr_features}

    # # Add metadata features (e.g., right vs. left, hand vs. foot)
    # for col in ["sensor_smartwatch", "sensor_vicon", "body_part_hand", "body_part_foot", "side_right", "side_left"]:
    #     features[col] = metadata_row[col]

    # Add sequence length as a feature
    # features["sequence_length"] = len(sequence)

    return features

# Main function to process all sequences
def process_sequences(data, filename, id_list, directory):
    if filename.endswith('.csv'):  # Check if the file is a CSV
        file_path = os.path.join(directory, filename)
        file_id = int(filename[:-4])  # Extract the file ID from the filename
        type = 0
        # Process only files where file_id is in test_metadata_ids
        if file_id in id_list:
            try:
                # Read the CSV file
                df = pd.read_csv(file_path)

                # Check if 'measurement type' column exists
                if 'measurement type' in df.columns:
                    type = 1
                    # Filter for acceleration data
                    df = df[df['measurement type'] == 'acceleration [m/s/s]']

                    # Add relevant columns based on test_metadata DataFrame
                    for col in columns:
                        val = test_metadata[test_metadata['id'] == file_id][col].values
                        if len(val) > 0:
                            df[col] = val[0]

                    # Drop the 'measurement type' column
                    df.drop(columns=['measurement type'], inplace=True)
                else:
                    type = 2
                    # Rename columns for location data
                    df.rename(columns={
                        'x [m]': 'x',
                        'y [m]': 'y',
                        'z [m]': 'z'
                    }, inplace=True)

                    # Add relevant columns based on test_metadata DataFrame
                    for col in columns:
                        val = test_metadata[test_metadata['id'] == file_id][col].values
                        if len(val) > 0:
                            df[col] = val[0]

            except Exception as e:
                print(f"Error processing file {file_path}: {e}")
                return None

    # Process the sequence and extract features
    features = process_sensor_data(df)
    for col in features.keys():
        df[col] = features[col]

    # Return sequence and file_id (no label for test data)
    seq = torch.tensor(df.values, dtype=torch.float32).permute(1, 0)
    return seq, file_id


# Preprocess the data directory
directory = '/kaggle/temp/data/unlabeled/unlabeled/'
columns = ['sensor_smartwatch', 'sensor_vicon', 'body_part_hand', 'body_part_foot', 'side_right', 'side_left']


test_ids = list(test_metadata['id'])

Dataset & Dataloader for test(without labels):¶

In [ ]:
class ProcessedSequenceDataset(Dataset):
    def __init__(self, data_dir, id_list, max_len, padding_value=0.0):
        """
        Dataset that preprocesses features (scaling and padding) in the initializer.
        Args:
        - id_list: List of IDs for the sequences.
        - max_len: Maximum sequence length for padding.
        """
        self.files = [f'{ID}.csv' for ID in id_list]
        self.id_list = id_list
        self.data_dir = data_dir
        self.max_len = max_len
        self.padding_value = padding_value

    def __len__(self):
        return len(self.files)

    def __getitem__(self, idx):
        """
        Get a single sequence and its file ID.
        """
        filename = self.files[idx]
        seq, file_id = process_sequences(test_metadata, filename, self.id_list, self.data_dir)
        pad_seq = self.pad_to_length(seq, self.max_len, self.padding_value)
        return pad_seq, file_id

    @staticmethod
    def pad_to_length(sequence, target_length, padding_value=0.0):
        seq_length = sequence.size(1)
        if seq_length < target_length:
            padding_needed = target_length - seq_length
            padded_sequence = torch.nn.functional.pad(sequence, (0, padding_needed), value=padding_value)
        else:
            padded_sequence = sequence[:, :target_length]
        return padded_sequence

    @staticmethod
    def collate_fn(batch):
        sequences, file_ids = zip(*batch)
        padded_sequences = pad_sequence(sequences, batch_first=True)
        return padded_sequences, file_ids
data_dir = '/kaggle/temp/data/unlabeled/unlabeled/'

# Initialize the test dataset and DataLoader
test_dataset = ProcessedSequenceDataset(data_dir=directory, id_list=test_ids, max_len=max_len)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, collate_fn=ProcessedSequenceDataset.collate_fn, num_workers=4)
In [ ]:
from tqdm import tqdm

# Set the model to evaluation mode
activity_classes = [
    "brushing_teeth", "idle", "preparing_sandwich", "reading_book",
    "stairs_down", "stairs_up", "typing", "using_phone", "using_remote_control",
    "walking_freely", "walking_holding_a_tray", "walking_with_handbag",
    "walking_with_hands_in_pockets", "walking_with_object_underarm",
    "washing_face_and_hands", "washing_mug", "washing_plate", "writing"
]

# Start evaluation
all_preds = []
all_ids = []
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Progress bar added using tqdm
with torch.no_grad():
    for batch in tqdm(test_loader, desc="Evaluating", leave=True):
        x, ids = batch
        x = x.to(device)

        # Get predictions (probabilities)
        y_hat = torch.nn.functional.softmax(model(x), dim=1)  # Use softmax for probabilities

        # Store predictions and IDs
        all_preds.append(y_hat.cpu())
        all_ids.extend(ids)

# Combine predictions and IDs
all_preds = torch.cat(all_preds).numpy()

# Create the DataFrame for submission
test_results = pd.DataFrame(all_preds, columns=activity_classes)
test_results.insert(0, "sample_id", all_ids)  # Add sample_id as the first column

# Save predictions to a CSV file
submission_path = "/kaggle/working/sample_submission_clean_lstm.csv"
test_results.to_csv(submission_path, index=False)

# Log results to MLflow
with mlflow.start_run():
    mlflow.log_artifact(submission_path)
    print(f"Test predictions saved to {submission_path} and logged to MLflow.")

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Test predictions saved to /kaggle/working/sample_submission_clean_lstm.csv and logged to MLflow.
In [ ]:
# Load the CSV file
file_path = "/kaggle/working/sample_submission_clean_lstm.csv"  # Replace with the path to your CSV file
df = pd.read_csv(file_path)

output_path = "/kaggle/working/sample_submission_clean_lstm.csv"
df_filled = df.ffill()

# Save the updated DataFrame to a new CSV
df_filled.to_csv(output_path, index=False)

print(f"File saved to {output_path}")

File saved to /kaggle/working/sample_submission_clean_lstm.csv

Self-Supervised Pretraining Task: Masking for Pretraining¶

Overview of the Self-Supervised Task¶

The chosen self-supervised task for pretraining is masking-based sequence prediction. This task involves:

  1. Randomly masking a portion of the input sequence.
  2. Training the model to predict the masked values, while leveraging the unmasked portions of the sequence for context.
  3. Using Mean Squared Error (MSE) loss to focus on reconstructing the masked values.

This approach allows the model to learn representations that capture temporal and contextual dependencies in the sequence data without requiring explicit labels.

Key Components¶

  1. Masking Logic:

    • A portion of the sequence is masked with a probability (mask_prob=0.2 in this case).
    • Masking is applied to only specific columns (e.g., the first three sensor axes), while ensuring padding values are not masked.

  2. Dataset:

    • The dataset includes time-series sequences with padding to handle variable lengths.
    • Each sequence is processed to create:
      • A masked sequence (input to the model).
      • The original sequence (used as the target for loss computation).
      • A masking mask (indicates which positions in the sequence were masked).

  3. Loss Function:

    • A custom loss function is used to compute the MSE only on the masked positions, ignoring the padded values.
    • This ensures the model learns to predict only the masked parts of the sequence.

Model Architecture¶

  1. LSTM Backbone:

    • A single-directional LSTM serves as the backbone for capturing temporal dependencies in the sequence data.
    • The LSTM outputs are passed through a fully connected layer to predict the masked values.

  2. Fine-Tuning Strategy:

    • After pretraining, the model is fine-tuned on the labeled classification task.
    • For the fine-tuning phase, the LSTM layers can be frozen initially to focus on training the classification layers.

Training Details¶

  1. Pretraining:

    • The model is pretrained on the unsupervised masking task for 7 epochs.
    • The pretraining dataset is derived from unlabeled sequence data, ensuring the model learns generalized representations.

  2. Fine-Tuning:

    • The pretrained LSTM layers are loaded into a new model for the classification task.
    • Initially, the LSTM layers are frozen for 5 epochs, and only the classification head is trained.
    • Afterward, the entire model is fine-tuned end-to-end for better performance.

Dataset Creation:¶

In [ ]:
class ProcessedSequenceDatasetMasking(Dataset):
    def __init__(self, data_dir, id_list, max_len, padding_value=0.0, mask_prob = 0.2):
        """
        Dataset that preprocesses features (scaling and padding) in the initializer.
        Args:
        - features: List of DataFrames (one per sequence).
        - labels: List of corresponding labels for the sequences.
        - padding_value: Value to use for padding.
        """
        #convert features to tensors
        self.files = [f'{ID}.csv' for ID in id_list]
        self.padding_value = padding_value
        self.id_list = id_list
        self.data_dir = data_dir
        self.max_len = max_len
        self.mask_prob = mask_prob

    def __len__(self):
        return len(self.files)


    def __getitem__(self, idx):
        """
        Get a single sequence, its mask, and the original sequence for unsupervised training.
        Only masks values from the first 3 columns.
        """
        filename = self.files[idx]
        seq, _ = process_sequences(train, filename, self.id_list, self.data_dir)

        # Create a padded sequence and the padding mask
        pad_seq = pad_to_length(seq, self.max_len, self.padding_value)
        padding_mask = (torch.tensor(pad_seq) == 0.0)  # Mask for padding values

        # Create a random mask only for the first 3 columns (seq_len, 3)
        random_mask = torch.rand(pad_seq.shape[0], 3) < self.mask_prob  # Shape (seq_len, 3)

        # Masking mask (True for masked positions, False for padding)
        masking_mask = random_mask & ~padding_mask[:, :3]  # Apply mask only to the first 3 columns

        # Expand the masking mask to the shape of the entire sequence (pad_seq.shape)
        full_masking_mask = torch.cat([masking_mask, torch.zeros(pad_seq.shape[0], pad_seq.shape[1] - 3, dtype=torch.bool)], dim=1)

        # Apply the mask to the sequence (mask only the first 3 columns)
        masked_seq = pad_seq.clone()  # Make a copy of the padded sequence
        masked_seq[:, :3] = masked_seq[:, :3] * ~full_masking_mask[:, :3]  # Apply mask only to the first 3 columns

        # Return both the masked sequence, original sequence (target), and the masking mask
        return masked_seq, pad_seq, full_masking_mask
In [ ]:
data_dir = '/kaggle/temp/data/unlabeled/unlabeled/'
mask_prob = 0.2
train_dataset = ProcessedSequenceDatasetMasking(data_dir, train_ids, max_len, mask_prob = mask_prob)
val_dataset = ProcessedSequenceDatasetMasking(data_dir, val_ids, max_len, mask_prob = mask_prob)
batch_size = 32

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4)

Masked LSTM:¶

In [ ]:
class SimpleLSTM(pl.LightningModule):
    def __init__(self, input_dim, hidden_dim, num_layers=3, bidirectional=True, learning_rate=0.001):
        super(SimpleLSTM, self).__init__()
        self.save_hyperparameters()

        self.lstm = nn.LSTM(
            input_dim,
            hidden_dim,
            num_layers=num_layers,
            bidirectional=bidirectional,
            batch_first=True
        )
        lstm_output_dim = hidden_dim * 2 if bidirectional else hidden_dim
        self.fc = nn.Linear(lstm_output_dim, input_dim)  # Output dimension matches input size
        self.learning_rate = learning_rate
        self.print_flag = True

    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        output = self.fc(lstm_out)  # Output sequence, same length as input
        return output

    def training_step(self, batch, batch_idx):
        x, actual, masking = batch  # Get the input (already masked) and the mask
        y_hat = self(x)  # Forward pass on the masked sequence
        # Compute the loss on unmasked positions
        loss = self.compute_loss(y_hat, actual, masking)
        self.log("train_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
        return loss

    def validation_step(self, batch, batch_idx):
        masked_seq, actual, mask = batch  # masked_seq is the input and mask is the true output (without padding)
        # Forward pass
        y_hat = self(masked_seq)
        # Calculate the loss (mean squared error or other loss function suited for sequence generation)
        loss = self.compute_loss(y_hat, actual, mask)
        # Log the loss
        self.log("val_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
        return loss

    def configure_optimizers(self):
        return optim.Adam(self.parameters(), lr=self.learning_rate)


    def compute_loss(self, y_hat, original_seq, masking_mask):
        """
        Compute the loss, only on the masked positions, and exclude padding positions.
        """
        # Convert masking mask to float (True becomes 1, False becomes 0)
        masking_mask = masking_mask.float()
        if self.print_flag:
            print_masked_values(y_hat, original_seq, masking_mask)
            self.print_flag = False
        # Use MSE loss for unsupervised prediction task
        loss = nn.MSELoss(reduction="none")(y_hat, original_seq)  # Compute MSE loss for each position

        # Mask the loss to only include the masked positions (where masking_mask is True)
        masked_loss = loss * masking_mask  # Only include loss for masked positions

        res = masked_loss.sum() / masking_mask.sum()

        return res


    def on_train_epoch_end(self):
        """
        Called at the end of each training epoch.
        """
        # Reactivate the flag for debugging the first batch of the next epoch
        self.print_flag = True
In [ ]:
import warnings
warnings.filterwarnings("ignore")

# Initialize the model
max_len = train_loader.dataset.max_len  # Defined in `ProcessedSequenceDataset`
input_dim = max_len
hidden_dim = 64
learning_rate = 0.001

model = SimpleLSTM(
    input_dim=input_dim,
    hidden_dim=hidden_dim,
    num_layers=2,
    bidirectional=False,
    learning_rate=learning_rate
)
# Setup MLflow
mlflow.set_experiment("Masking_LSTM_Experiment")

# MLflow Logger
mlflow_logger = MLFlowLogger(
    experiment_name="Masking_LSTM_Experiment",
    run_name="LSTM_Run"
)


# Trainer setup
trainer = pl.Trainer(
    max_epochs=7,
    precision=16,
    logger=mlflow_logger,  # Add MLflow logger here
    accelerator="gpu" if torch.cuda.is_available() else "cpu",
    devices=torch.cuda.device_count()
)

# Start MLflow run
with mlflow.start_run() as run:
    # Log parameters to MLflow
    mlflow.log_param("input_dim", input_dim)
    mlflow.log_param("hidden_dim", hidden_dim)
    mlflow.log_param("num_layers", 3)
    mlflow.log_param("bidirectional", False)
    mlflow.log_param("learning_rate", learning_rate)
    mlflow.log_param("max_len", max_len)
    mlflow.log_param("batch_size", 32)

    # Train the model
    trainer.fit(model, train_loader, val_loader)

    # Validate the model
    val_results = trainer.validate(model, val_loader)

    # Log metrics to MLflow
    for metric in val_results:
        for k, v in metric.items():
            mlflow.log_metric(k, v)

    # Save the best model to MLflow
    mlflow.pytorch.log_model(model, artifact_path="model")

    print(val_results)

Save the pre-trained model:¶

In [ ]:
trainer.save_checkpoint("trained_model.ckpt")
checkpoint = torch.load("./trained_model.ckpt")

Dataset class:¶

In [ ]:
class ProcessedSequenceDataset(Dataset):
    def __init__(self, data_dir, labels, id_list, max_len, padding_value=0.0):
        """
        Dataset that preprocesses features (scaling and padding) in the initializer.
        Args:
        - features: List of DataFrames (one per sequence).
        - labels: List of corresponding labels for the sequences.
        - padding_value: Value to use for padding.
        """
        #convert features to tensors
        self.files = [f'{ID}.csv' for ID in id_list]
        self.labels = labels
        self.padding_value = padding_value
        self.id_list = id_list
        self.data_dir = data_dir
        self.max_len = max_len

    def __len__(self):
        return len(self.files)


    def __getitem__(self, idx):
        """
        Get a single sequence and its label.
        """
        filename = self.files[idx]
        seq, label =  process_sequences(train, filename, self.id_list, self.data_dir)
        pad_seq = pad_to_length(seq, self.max_len, self.padding_value)
        return pad_seq, label

    def collate_fn(batch):
        sequences, labels = zip(*batch)

        # Pad sequences along the time dimension (dim=0)
        padded_sequences = pad_sequence(sequences, batch_first=True)

        labels = torch.tensor(labels)
        res = (padded_sequences, labels)
        return res
In [ ]:
data_dir = '/kaggle/temp/data/unlabeled/unlabeled/'

train_dataset = ProcessedSequenceDataset(data_dir,labels_dict, train_ids,max_len)
val_dataset = ProcessedSequenceDataset(data_dir,labels_dict,val_ids, max_len)
batch_size = 32

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=ProcessedSequenceDataset.collate_fn, num_workers=4)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, collate_fn=ProcessedSequenceDataset.collate_fn, num_workers=4)

Pre-Trained LSTM:¶

In [ ]:
# Setup MLflow
mlflow.set_experiment("PreTrained_LSTM")

# MLflow Logger
mlflow_logger = MLFlowLogger(
    experiment_name="PreTrained_LSTM",
    run_name="LSTM_Run"
)

def freeze_layers(model):
    for name, param in model.named_parameters():
        if "lstm" in name:  # Freeze LSTM layers
            param.requires_grad = False

def unfreeze_layers(model):
    for name, param in model.named_parameters():
        if "lstm" in name:  # Unfreeze LSTM layers
            param.requires_grad = True


# LSTM Model
class FineTunedLSTM(SimpleLSTM):
    def __init__(self, input_dim, hidden_dim, num_classes, freeze_epochs=3, **kwargs):
        super(FineTunedLSTM, self).__init__(input_dim=input_dim, hidden_dim=hidden_dim, num_classes=num_classes, **kwargs)

        self.freeze_epochs = freeze_epochs  # Number of epochs to freeze layers
        lstm_output_dim = hidden_dim * 2 if self.hparams.bidirectional else hidden_dim
        # self.layer_norm = nn.LayerNorm(lstm_output_dim)  # Layer Normalization
        # self.attention = nn.MultiheadAttention(embed_dim=lstm_output_dim, num_heads=4, batch_first=True)  # Attention layer
        self.fc = nn.Linear(lstm_output_dim, num_classes)

    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        lstm_out = lstm_out[:, -1, :]
        # norm_out = self.layer_norm(lstm_out)  # Apply layer normalization
        # # Apply self-attention
        # attention_out, _ = self.attention(norm_out, norm_out, norm_out)
        output = self.fc(lstm_out)  # Output sequence, same length as input
        return output

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = nn.CrossEntropyLoss()(y_hat, y)
        self.log("train_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
        return loss

    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = nn.CrossEntropyLoss()(y_hat, y)
        acc = (torch.argmax(y_hat, dim=1) == y).float().mean()
        self.log("val_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
        self.log("val_acc", acc, on_step=False, on_epoch=True, prog_bar=True, logger=True)
        return loss

    def configure_optimizers(self):
        optimizer = optim.Adam([
            {"params": self.lstm.parameters(), "lr": 1e-4},  # Lower learning rate for frozen layers
            {"params": self.fc.parameters(), "lr": 1e-3}     # Higher learning rate for new layers
        ])
        return optimizer

    def on_train_epoch_start(self):
        # Freeze layers for the first few epochs
        if self.current_epoch < self.freeze_epochs:
            freeze_layers(self)
            print(f"Epoch {self.current_epoch}: Freezing layers.")
        else:
            unfreeze_layers(self)
            print(f"Epoch {self.current_epoch}: Unfreezing layers.")

# Define input dimensions and other parameters
max_len = train_loader.dataset.max_len  # Defined in `ProcessedSequenceDataset`
input_dim = max_len
hidden_dim = 64
num_classes = len(set(labels_dict.values()))  # Number of unique labels in your dataset
learning_rate = 0.001

# Initialize the model
model = FineTunedLSTM(
    input_dim=input_dim,
    hidden_dim=hidden_dim,
    num_classes=num_classes,
    num_layers=2,
    bidirectional=False,
    learning_rate=learning_rate,
    freeze_epochs=5
)

pretrained_dict = checkpoint['state_dict']
model_dict = model.state_dict()

# Filter out the `fc` layer weights
pretrained_dict = {k: v for k, v in pretrained_dict.items() if "fc" not in k}

# Update the current model's weights with the pre-trained ones
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)

# Trainer setup
trainer = pl.Trainer(
    max_epochs=15,
    logger=mlflow_logger,  # Add MLflow logger here
    accelerator="gpu" if torch.cuda.is_available() else "cpu",
    devices=torch.cuda.device_count()
)

# Start MLflow run
with mlflow.start_run() as run:
    # Log parameters to MLflow
    mlflow.log_param("input_dim", input_dim)
    mlflow.log_param("hidden_dim", hidden_dim)
    mlflow.log_param("num_layers", 3)
    mlflow.log_param("bidirectional", False)
    mlflow.log_param("learning_rate", learning_rate)
    mlflow.log_param("max_len", max_len)
    mlflow.log_param("batch_size", 32)

    # Train the model
    trainer.fit(model, train_loader, val_loader)

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In [ ]:
from tqdm.notebook import tqdm

# Set the model to evaluation mode
activity_classes = [
    "brushing_teeth", "idle", "preparing_sandwich", "reading_book",
    "stairs_down", "stairs_up", "typing", "using_phone", "using_remote_control",
    "walking_freely", "walking_holding_a_tray", "walking_with_handbag",
    "walking_with_hands_in_pockets", "walking_with_object_underarm",
    "washing_face_and_hands", "washing_mug", "washing_plate", "writing"
]

# Start evaluation
all_preds = []
all_ids = []
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Progress bar added using tqdm
with torch.no_grad():
    for batch in tqdm(test_loader, desc="Evaluating", leave=True):
        x, ids = batch
        x = x.to(device)

        # Get predictions (probabilities)
        y_hat = torch.nn.functional.softmax(model(x), dim=1)  # Use softmax for probabilities

        # Store predictions and IDs
        all_preds.append(y_hat.cpu())
        all_ids.extend(ids)

# Combine predictions and IDs
all_preds = torch.cat(all_preds).numpy()

# Create the DataFrame for submission
test_results = pd.DataFrame(all_preds, columns=activity_classes)
test_results.insert(0, "sample_id", all_ids)  # Add sample_id as the first column
test_results = test_results.ffill()
# Save predictions to a CSV file
submission_path = "/kaggle/working/sample_submission_fin.csv"
test_results.to_csv(submission_path, index=False)

# Log results to MLflow
with mlflow.start_run():
    mlflow.log_artifact(submission_path)
    print(f"Test predictions saved to {submission_path} and logged to MLflow.")

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In [ ]:
from pytorch_lightning.callbacks import Callback

class MetricsTracker(Callback):
    def __init__(self):
        self.train_losses = []
        self.val_losses = []
        self.val_accuracies = []

    def on_train_epoch_end(self, trainer, pl_module):
        # Get the training loss from the logged metrics
        train_loss = trainer.callback_metrics.get("train_loss")
        if train_loss:
            self.train_losses.append(train_loss.item())

    def on_validation_epoch_end(self, trainer, pl_module):
        # Get the validation loss and accuracy from the logged metrics
        val_loss = trainer.callback_metrics.get("val_loss")
        val_acc = trainer.callback_metrics.get("val_acc")
        if val_loss:
            self.val_losses.append(val_loss.item())
        if val_acc:
            self.val_accuracies.append(val_acc.item())

    def save_metrics_to_file(self, file_path):
        # Save metrics to a text file
        with open(file_path, "w") as f:
            f.write("Training and Validation Metrics\n")
            f.write("=" * 40 + "\n")
            f.write("Epoch, Train Loss, Validation Loss, Validation Accuracy\n")
            for i in range(len(self.train_losses)):
                train_loss = self.train_losses[i] if i < len(self.train_losses) else "N/A"
                val_loss = self.val_losses[i] if i < len(self.val_losses) else "N/A"
                val_acc = self.val_accuracies[i] if i < len(self.val_accuracies) else "N/A"
                f.write(f"{i+1}, {train_loss}, {val_loss}, {val_acc}\n")
        print(f"Metrics saved to {file_path}")

# metrics_tracker = MetricsTracker()

# with mlflow.start_run():


#     # Validate the model
#     val_results = trainer.validate(model, val_loader)

#     # Log validation results
#     for metric in val_results:
#         for k, v in metric.items():
#             mlflow.log_metric(k, v)

# # Save metrics to file
# metrics_tracker.save_metrics_to_file("/kaggle/working/metrics_log.txt")


# Visualizations
# Plot training and validation loss
# metrics = trainer.logged_metrics
# train_losses = metrics["train_loss"]
# val_losses = metrics["val_loss"]
# train_losses = metrics_tracker.train_losses
# validation_losses = metrics_tracker.val_losses

# Pad train_losses to match the length of validation_losses
if len(train_losses) < len(validation_losses):
    padding_value = train_losses[-1] if train_losses else 0  # Use last value or 0 if empty
    train_losses += [padding_value] * (len(validation_losses) - len(train_losses))

# metrics_tracker.train_losses = train_losses
# Visualize Training and Validation Loss
plt.figure(figsize=(10, 6))
# plt.plot(metrics_tracker.train_losses, label="Train Loss")
plt.plot(val_losses, label="Validation Loss")
plt.title("Loss Over Epochs")
plt.xlabel("Epoch")
plt.ylabel("Loss")
# plt.legend()
plt.grid(True)
plt.show()

# Visualize Validation Accuracy
plt.figure(figsize=(10, 6))
plt.plot(val_accuracies, label="Validation Accuracy")
plt.title("Validation Accuracy Over Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend()
plt.grid(True)
plt.show()

# Function to extract predictions from the model
activity_classes = [
    "brushing_teeth", "idle", "preparing_sandwich", "reading_book",
    "stairs_down", "stairs_up", "typing", "using_phone", "using_remote_control",
    "walking_freely", "walking_holding_a_tray", "walking_with_handbag",
    "walking_with_hands_in_pockets", "walking_with_object_underarm",
    "washing_face_and_hands", "washing_mug", "washing_plate", "writing"
]
def extract_predictions(model, dataloader, activity_classes):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    model.eval()

    all_predictions = []
    all_labels = []
    all_probabilities = []

    with torch.no_grad():
        for batch in dataloader:
            inputs, labels = batch
            inputs = inputs.to(device)
            outputs = model(inputs)

            probabilities = F.softmax(outputs, dim=1).cpu().numpy()
            predicted_classes = np.argmax(probabilities, axis=1)

            all_predictions.extend(predicted_classes)
            all_labels.extend(labels.numpy())
            all_probabilities.extend(probabilities)

    return np.array(all_predictions), np.array(all_labels), np.array(all_probabilities)
# Extract predictions, true labels, and probabilities
predictions, true_labels, probabilities = extract_predictions(model, val_loader, activity_classes)

# High confidence threshold (e.g., >90%)
high_confidence_threshold = 0.9
# Low confidence threshold (e.g., <60%)
low_confidence_threshold = 0.6

# Identify good classifications (correct predictions with high confidence)
good_indices = [
    i for i in range(len(predictions))
    if predictions[i] == true_labels[i] and max(probabilities[i]) >= high_confidence_threshold
]

# Identify bad classifications (incorrect predictions with high confidence)
bad_indices = [
    i for i in range(len(predictions))
    if predictions[i] != true_labels[i] and max(probabilities[i]) >= high_confidence_threshold
]

# Identify uncertain predictions (confidence below threshold)
uncertain_indices = [
    i for i in range(len(predictions))
    if max(probabilities[i]) < low_confidence_threshold
]

# Function to visualize examples with activity names
def plot_example(sequence, true_label_idx, predicted_label_idx, confidence, title, activity_classes, idx):
    true_label = activity_classes[true_label_idx]
    predicted_label = activity_classes[predicted_label_idx]

    plt.figure(figsize=(10, 4))
    for i, axis in enumerate(["X", "Y", "Z"]):
        plt.plot(sequence[:, i], label=f"{axis}-axis")
    plt.title(
        f"{title}\nID: {idx}, True Activity: {true_label}, Predicted: {predicted_label}, Confidence: {confidence:.2f}"
    )
    plt.xlabel("Time Step")
    plt.ylabel("Sensor Value")
    plt.legend()
    plt.grid(True)
    plt.show()

# Visualize good classifications
print("Good Classifications:")
for idx in good_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Good Classification",
        activity_classes,
        idx
    )

# Visualize bad classifications
print("Bad Classifications:")
for idx in bad_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Bad Classification",
        activity_classes,
        idx
    )

# Visualize uncertain predictions
print("Uncertain Predictions:")
for idx in uncertain_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Uncertain Prediction",
        activity_classes,
    idx)
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Good Classifications:
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Bad Classifications:
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Uncertain Predictions:
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In [ ]:

Analysis of Results: Self-Supervised Masking LSTM vs. Regular LSTM¶

Overview¶

This analysis compares the Self-Supervised Masking LSTM results to the Regular LSTM model. The models were evaluated on the same dataset for human activity recognition (HAR) tasks. Key aspects such as accuracy, loss, and classification quality are analyzed.

Self-Supervised Masking LSTM Results¶

Validation Performance¶

  • Validation Accuracy: 48.25%

  • Validation Loss: ~1.4

  • Good Classifications:

    • High confidence in activities with distinct patterns like:
      • walking_holding_a_tray (Confidence ~0.99).
      • using_remote_control (Confidence ~0.94).
    • Demonstrated capability to identify less ambiguous activities.

  • Bad Classifications:

    • Misclassifications in activities with overlapping patterns:
      • brushing_teeth misclassified as walking_holding_a_tray (Confidence ~0.96).
      • stairs_up misclassified as stairs_down (Confidence ~0.92).

  • Uncertain Predictions:

    • Activities with low confidence include:
      • walking_holding_a_tray predicted as using_phone (Confidence ~0.26).
      • walking_with_handbag predicted as walking_with_handbag (Confidence ~0.36).

Key Observations¶

  • The Masking LSTM model struggles to distinguish subtle activity variations due to weaker supervised signals during training.
  • High-confidence predictions are skewed towards distinct activities with less variability.
  • Activities with fewer samples or high intra-class variability were particularly challenging for the Masking Model.

Regular LSTM Results¶

Validation Performance¶

  • Validation Accuracy: 81%

  • Validation Loss: 0.52

  • Good Classifications:

    • High confidence for activities like:
      • walking_with_hands_in_pockets.
      • reading_book.
    • Demonstrated strong generalization for activities with distinct motion patterns.

  • Bad Classifications:

    • Misclassification of overlapping activities:
      • washing_plate predicted as washing_mug.
      • walking_with_object_underarm predicted as walking_with_handbag.

  • Uncertain Predictions:

    • Few uncertain predictions due to stronger label supervision.

Key Observations¶

  • The Regular LSTM significantly outperforms the Masking LSTM in distinguishing subtle differences between activities.
  • The model effectively generalizes across the validation set with minimal overfitting.
  • Challenges remain for classifying highly similar activities due to overlapping patterns in sensor data.

Comparative Analysis¶

Metric Self-Supervised Masking LSTM Regular LSTM Key Insights
Validation Accuracy 48.25% 81% Regular LSTM achieves nearly 2x accuracy.
Validation Loss ~1.4 0.52 Regular LSTM generalizes better with lower loss.

Suggestions for Improving Model Results¶

Prioritized Improvements¶

  1. Use Learning Rate Scheduler (Implemented Priority)

    • Why: Learning rate schedulers dynamically adjust the learning rate during training, avoiding overshooting and improving convergence.
    • How: Implement a scheduler to reduce the learning rate when the validation loss plateaus, ensuring steady improvement.

  2. Add Dropout for Regularization

    • Why: Dropout reduces overfitting by deactivating random neurons during training, encouraging the model to generalize better.
    • How: Add dropout layers after the LSTM or fully connected layers to improve robustness.

  3. Experiment with Bidirectional LSTM

    • Why: Bidirectional LSTMs enhance the model's ability to capture dependencies from both past and future contexts, which can improve performance on sequential data.
    • How: Enable bidirectional LSTM layers to model richer temporal dependencies.

  4. Fine-Tune Pretraining Strategy

    • Why: Refining pretraining tasks improves the model's feature extraction capabilities and reduces downstream task loss.
    • How:
      • Adjust masking probabilities in the self-supervised task to better align with the classification task.
      • Gradually unfreeze layers during training to fine-tune task-specific representations.

  5. Introduce Early Stopping Based on Validation Loss

    • Why: Prevent overfitting and reduce unnecessary training epochs.
    • How: Monitor validation loss and stop training if no improvement is observed for a set number of epochs.

  6. Use Label Smoothing

    • Why: Reduces overconfidence in predictions by smoothing class probabilities, leading to improved generalization.
    • How: Add a small smoothing factor in the loss function for classification tasks.

  7. Augment Training Data

    • Why: Sequential data augmentation can improve the model’s ability to handle noisy or diverse inputs.
    • How:
      • Add Gaussian noise to sequences.
      • Perform random cropping or padding on sequences to simulate variability.

Implementation Roadmap¶

  1. Implement the learning rate scheduler (completed).
  2. Add dropout to reduce overfitting.

Fine-Tuned Pre-Trained LSTM:¶

In [ ]:
# Setup MLflow
mlflow.set_experiment("PreTrained_LSTM")

# MLflow Logger
mlflow_logger = MLFlowLogger(
    experiment_name="PreTrained_LSTM",
    run_name="LSTM_Run"
)

def freeze_layers(model):
    for name, param in model.named_parameters():
        if "lstm" in name:  # Freeze LSTM layers
            param.requires_grad = False

def unfreeze_layers(model):
    for name, param in model.named_parameters():
        if "lstm" in name:  # Unfreeze LSTM layers
            param.requires_grad = True


# LSTM Model
class FineTunedLSTM(SimpleLSTM):
    def __init__(self, input_dim, hidden_dim, num_classes, freeze_epochs=3, **kwargs):
        super(FineTunedLSTM, self).__init__(input_dim=input_dim, hidden_dim=hidden_dim, num_classes=num_classes, **kwargs)

        self.freeze_epochs = freeze_epochs  # Number of epochs to freeze layers
        lstm_output_dim = hidden_dim * 2 if self.hparams.bidirectional else hidden_dim
        self.fc = nn.Linear(lstm_output_dim, num_classes)
        self.dropout = nn.Dropout(0.3)

    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        lstm_out = self.dropout(lstm_out[:, -1, :])
        output = self.fc(lstm_out)  # Output sequence, same length as input
        return output

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = nn.CrossEntropyLoss()(y_hat, y)
        self.log("train_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
        return loss

    def validation_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = nn.CrossEntropyLoss()(y_hat, y)
        acc = (torch.argmax(y_hat, dim=1) == y).float().mean()
        self.log("val_loss", loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
        self.log("val_acc", acc, on_step=False, on_epoch=True, prog_bar=True, logger=True)
        return loss

    def configure_optimizers(self):
        optimizer = optim.Adam([
            {"params": self.lstm.parameters(), "lr": 1e-4},  # Lower learning rate for frozen layers
            {"params": self.fc.parameters(), "lr": 1e-3}     # Higher learning rate for new layers
        ])
        scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=0.5, patience=2)
        return {"optimizer": optimizer, "lr_scheduler": scheduler, "monitor": "val_loss"}

    def on_train_epoch_start(self):
        # Freeze layers for the first few epochs
        if self.current_epoch < self.freeze_epochs:
            freeze_layers(self)
            print(f"Epoch {self.current_epoch}: Freezing layers.")
        else:
            unfreeze_layers(self)
            print(f"Epoch {self.current_epoch}: Unfreezing layers.")

# Define input dimensions and other parameters
max_len = train_loader.dataset.max_len  # Defined in `ProcessedSequenceDataset`
input_dim = max_len
hidden_dim = 64
num_classes = len(set(labels_dict.values()))  # Number of unique labels in your dataset
learning_rate = 0.001

# Initialize the model
model = FineTunedLSTM(
    input_dim=input_dim,
    hidden_dim=hidden_dim,
    num_classes=num_classes,
    num_layers=2,
    bidirectional=False,
    learning_rate=learning_rate,
    freeze_epochs=5
)

pretrained_dict = checkpoint['state_dict']
model_dict = model.state_dict()

# Filter out the `fc` layer weights
pretrained_dict = {k: v for k, v in pretrained_dict.items() if "fc" not in k}

# Update the current model's weights with the pre-trained ones
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)

# Trainer setup
trainer = pl.Trainer(
    max_epochs=15,
    logger=mlflow_logger,  # Add MLflow logger here
    accelerator="gpu" if torch.cuda.is_available() else "cpu",
    devices=torch.cuda.device_count()
)

# Start MLflow run
with mlflow.start_run() as run:
    # Log parameters to MLflow
    mlflow.log_param("input_dim", input_dim)
    mlflow.log_param("hidden_dim", hidden_dim)
    mlflow.log_param("num_layers", 3)
    mlflow.log_param("bidirectional", False)
    mlflow.log_param("learning_rate", learning_rate)
    mlflow.log_param("max_len", max_len)
    mlflow.log_param("batch_size", 32)

    # Train the model
    trainer.fit(model, train_loader, val_loader)

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Epoch 2: Freezing layers.

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Epoch 3: Freezing layers.

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Epoch 4: Freezing layers.

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Epoch 6: Unfreezing layers.

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Epoch 7: Unfreezing layers.

Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
Exception ignored in:     <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
if w.is_alive():Traceback (most recent call last):

  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
        assert self._parent_pid == os.getpid(), 'can only test a child process'self._shutdown_workers()

AssertionError  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
:     can only test a child processif w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    
assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'Exception ignored in: 
<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
AssertionErrorTraceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
: can only test a child process    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers

    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
Exception ignored in:     assert self._parent_pid == os.getpid(), 'can only test a child process'<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>

AssertionErrorTraceback (most recent call last):
:   File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
can only test a child process
    self._shutdown_workers()Exception ignored in: 
<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers

    Traceback (most recent call last):
if w.is_alive():  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__

      File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    self._shutdown_workers()assert self._parent_pid == os.getpid(), 'can only test a child process'

  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
AssertionError:     can only test a child processif w.is_alive():

  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    
assert self._parent_pid == os.getpid(), 'can only test a child process'AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process

Validation: |          | 0/? [00:00<?, ?it/s]
Epoch 8: Unfreezing layers.

Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process

Validation: |          | 0/? [00:00<?, ?it/s]
Epoch 9: Unfreezing layers.

Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
Exception ignored in:   File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>    
self._shutdown_workers()Traceback (most recent call last):

  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
      File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
self._shutdown_workers()    
if w.is_alive():  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers

    if w.is_alive():  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive

    assert self._parent_pid == os.getpid(), 'can only test a child process'  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive

    AssertionErrorassert self._parent_pid == os.getpid(), 'can only test a child process'
: AssertionErrorcan only test a child process: 
can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process

Validation: |          | 0/? [00:00<?, ?it/s]
Epoch 10: Unfreezing layers.

Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
assert self._parent_pid == os.getpid(), 'can only test a child process'    
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process

Validation: |          | 0/? [00:00<?, ?it/s]
Epoch 11: Unfreezing layers.

Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():Exception ignored in: 
<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive

    Traceback (most recent call last):
assert self._parent_pid == os.getpid(), 'can only test a child process'  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__

AssertionError    : can only test a child process
self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Exception ignored in: Traceback (most recent call last):
<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
Traceback (most recent call last):

  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
      File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    self._shutdown_workers()    assert self._parent_pid == os.getpid(), 'can only test a child process'

  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
AssertionError    : if w.is_alive():
can only test a child process  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    
assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers

    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process

Validation: |          | 0/? [00:00<?, ?it/s]
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>Traceback (most recent call last):

  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process

Epoch 12: Unfreezing layers.

Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
Exception ignored in:     <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>if w.is_alive():

Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
        assert self._parent_pid == os.getpid(), 'can only test a child process'self._shutdown_workers()

  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
AssertionError    if w.is_alive():
:   File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    can only test a child processassert self._parent_pid == os.getpid(), 'can only test a child process'

AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process

Validation: |          | 0/? [00:00<?, ?it/s]
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    Exception ignored in: if w.is_alive():<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>

  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
Traceback (most recent call last):
Exception ignored in:   File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
    Exception ignored in:     assert self._parent_pid == os.getpid(), 'can only test a child process'<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40><function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>


self._shutdown_workers()AssertionError: Traceback (most recent call last):
can only test a child process
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers

  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
        if w.is_alive():    self._shutdown_workers()

self._shutdown_workers()  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    
assert self._parent_pid == os.getpid(), 'can only test a child process'      File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    
if w.is_alive():Exception ignored in: AssertionError
<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
: 
can only test a child processTraceback (most recent call last):
    if w.is_alive():
assert self._parent_pid == os.getpid(), 'can only test a child process'  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__

    AssertionErrorself._shutdown_workers()Exception ignored in: : 
<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>can only test a child process

  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers

Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
          File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
if w.is_alive():Exception ignored in:     self._shutdown_workers()<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>assert self._parent_pid == os.getpid(), 'can only test a child process'


  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers

Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
      File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
: AssertionError        assert self._parent_pid == os.getpid(), 'can only test a child process'can only test a child processif w.is_alive():
self._shutdown_workers()
AssertionError

: Exception ignored in:   File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
can only test a child process<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers

    
    assert self._parent_pid == os.getpid(), 'can only test a child process'if w.is_alive():
Traceback (most recent call last):

AssertionError  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
:     can only test a child process    assert self._parent_pid == os.getpid(), 'can only test a child process'

self._shutdown_workers()AssertionError: can only test a child processException ignored in: 
<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>

Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
        self._shutdown_workers()if w.is_alive():

Exception ignored in:   File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
    
Traceback (most recent call last):
assert self._parent_pid == os.getpid(), 'can only test a child process'  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__

        assert self._parent_pid == os.getpid(), 'can only test a child process'AssertionError: self._shutdown_workers()
can only test a child processAssertionError

  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
: can only test a child process    
if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'Exception ignored in: 
<function _MultiProcessingDataLoaderIter.__del__ at 0x7f79d61bbf40>
AssertionErrorTraceback (most recent call last):
: can only test a child process  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1477, in __del__

    self._shutdown_workers()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1460, in _shutdown_workers
    if w.is_alive():
  File "/usr/lib/python3.10/multiprocessing/process.py", line 160, in is_alive
    assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process

Epoch 13: Unfreezing layers.

Validation: |          | 0/? [00:00<?, ?it/s]
Epoch 14: Unfreezing layers.

Validation: |          | 0/? [00:00<?, ?it/s]
In [ ]:
from tqdm.notebook import tqdm

# Set the model to evaluation mode
activity_classes = [
    "brushing_teeth", "idle", "preparing_sandwich", "reading_book",
    "stairs_down", "stairs_up", "typing", "using_phone", "using_remote_control",
    "walking_freely", "walking_holding_a_tray", "walking_with_handbag",
    "walking_with_hands_in_pockets", "walking_with_object_underarm",
    "washing_face_and_hands", "washing_mug", "washing_plate", "writing"
]

# Start evaluation
all_preds = []
all_ids = []
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Progress bar added using tqdm
with torch.no_grad():
    for batch in tqdm(test_loader, desc="Evaluating", leave=True):
        x, ids = batch
        x = x.to(device)

        # Get predictions (probabilities)
        y_hat = torch.nn.functional.softmax(model(x), dim=1)  # Use softmax for probabilities

        # Store predictions and IDs
        all_preds.append(y_hat.cpu())
        all_ids.extend(ids)

# Combine predictions and IDs
all_preds = torch.cat(all_preds).numpy()

# Create the DataFrame for submission
test_results = pd.DataFrame(all_preds, columns=activity_classes)
test_results = test_results.ffill()
test_results.insert(0, "sample_id", all_ids)  # Add sample_id as the first column

# Save predictions to a CSV file
submission_path = "/kaggle/working/sample_submission_fin.csv"
test_results.to_csv(submission_path, index=False)

# Log results to MLflow
with mlflow.start_run():
    mlflow.log_artifact(submission_path)
    print(f"Test predictions saved to {submission_path} and logged to MLflow.")

Evaluating:   0%|          | 0/584 [00:00<?, ?it/s]
Test predictions saved to /kaggle/working/sample_submission_fin.csv and logged to MLflow.
In [ ]:
from pytorch_lightning.callbacks import Callback

class MetricsTracker(Callback):
    def __init__(self):
        self.train_losses = []
        self.val_losses = []
        self.val_accuracies = []

    def on_train_epoch_end(self, trainer, pl_module):
        # Get the training loss from the logged metrics
        train_loss = trainer.callback_metrics.get("train_loss")
        if train_loss:
            self.train_losses.append(train_loss.item())

    def on_validation_epoch_end(self, trainer, pl_module):
        # Get the validation loss and accuracy from the logged metrics
        val_loss = trainer.callback_metrics.get("val_loss")
        val_acc = trainer.callback_metrics.get("val_acc")
        if val_loss:
            self.val_losses.append(val_loss.item())
        if val_acc:
            self.val_accuracies.append(val_acc.item())

    def save_metrics_to_file(self, file_path):
        # Save metrics to a text file
        with open(file_path, "w") as f:
            f.write("Training and Validation Metrics\n")
            f.write("=" * 40 + "\n")
            f.write("Epoch, Train Loss, Validation Loss, Validation Accuracy\n")
            for i in range(len(self.train_losses)):
                train_loss = self.train_losses[i] if i < len(self.train_losses) else "N/A"
                val_loss = self.val_losses[i] if i < len(self.val_losses) else "N/A"
                val_acc = self.val_accuracies[i] if i < len(self.val_accuracies) else "N/A"
                f.write(f"{i+1}, {train_loss}, {val_loss}, {val_acc}\n")
        print(f"Metrics saved to {file_path}")

metrics_tracker = MetricsTracker()

with mlflow.start_run():


    # Validate the model
    val_results = trainer.validate(model, val_loader)

    # Log validation results
    for metric in val_results:
        for k, v in metric.items():
            mlflow.log_metric(k, v)

# # Save metrics to file
metrics_tracker.save_metrics_to_file("/kaggle/working/metrics_log.txt")


# Visualizations
# Plot training and validation loss
# metrics = trainer.logged_metrics
# train_losses = metrics["train_loss"]
# val_losses = metrics["val_loss"]
train_losses = metrics_tracker.train_losses
validation_losses = metrics_tracker.val_losses

# Pad train_losses to match the length of validation_losses
if len(train_losses) < len(validation_losses):
    padding_value = train_losses[-1] if train_losses else 0  # Use last value or 0 if empty
    train_losses += [padding_value] * (len(validation_losses) - len(train_losses))

# metrics_tracker.train_losses = train_losses
# Visualize Training and Validation Loss
plt.figure(figsize=(10, 6))
# plt.plot(metrics_tracker.train_losses, label="Train Loss")
plt.plot(val_losses, label="Validation Loss")
plt.title("Loss Over Epochs")
plt.xlabel("Epoch")
plt.ylabel("Loss")
# plt.legend()
plt.grid(True)
plt.show()

# Visualize Validation Accuracy
plt.figure(figsize=(10, 6))
plt.plot(val_accuracies, label="Validation Accuracy")
plt.title("Validation Accuracy Over Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.legend()
plt.grid(True)
plt.show()

# Function to extract predictions from the model
activity_classes = [
    "brushing_teeth", "idle", "preparing_sandwich", "reading_book",
    "stairs_down", "stairs_up", "typing", "using_phone", "using_remote_control",
    "walking_freely", "walking_holding_a_tray", "walking_with_handbag",
    "walking_with_hands_in_pockets", "walking_with_object_underarm",
    "washing_face_and_hands", "washing_mug", "washing_plate", "writing"
]
def extract_predictions(model, dataloader, activity_classes):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    model.eval()

    all_predictions = []
    all_labels = []
    all_probabilities = []

    with torch.no_grad():
        for batch in dataloader:
            inputs, labels = batch
            inputs = inputs.to(device)
            outputs = model(inputs)

            probabilities = F.softmax(outputs, dim=1).cpu().numpy()
            predicted_classes = np.argmax(probabilities, axis=1)

            all_predictions.extend(predicted_classes)
            all_labels.extend(labels.numpy())
            all_probabilities.extend(probabilities)

    return np.array(all_predictions), np.array(all_labels), np.array(all_probabilities)
# Extract predictions, true labels, and probabilities
predictions, true_labels, probabilities = extract_predictions(model, val_loader, activity_classes)

# High confidence threshold (e.g., >90%)
high_confidence_threshold = 0.9
# Low confidence threshold (e.g., <60%)
low_confidence_threshold = 0.6

# Identify good classifications (correct predictions with high confidence)
good_indices = [
    i for i in range(len(predictions))
    if predictions[i] == true_labels[i] and max(probabilities[i]) >= high_confidence_threshold
]

# Identify bad classifications (incorrect predictions with high confidence)
bad_indices = [
    i for i in range(len(predictions))
    if predictions[i] != true_labels[i] and max(probabilities[i]) >= high_confidence_threshold
]

# Identify uncertain predictions (confidence below threshold)
uncertain_indices = [
    i for i in range(len(predictions))
    if max(probabilities[i]) < low_confidence_threshold
]

# Function to visualize examples with activity names
def plot_example(sequence, true_label_idx, predicted_label_idx, confidence, title, activity_classes, idx):
    true_label = activity_classes[true_label_idx]
    predicted_label = activity_classes[predicted_label_idx]

    plt.figure(figsize=(10, 4))
    for i, axis in enumerate(["X", "Y", "Z"]):
        plt.plot(sequence[:, i], label=f"{axis}-axis")
    plt.title(
        f"{title}\nID: {idx}, True Activity: {true_label}, Predicted: {predicted_label}, Confidence: {confidence:.2f}"
    )
    plt.xlabel("Time Step")
    plt.ylabel("Sensor Value")
    plt.legend()
    plt.grid(True)
    plt.show()

# Visualize good classifications
print("Good Classifications:")
for idx in good_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Good Classification",
        activity_classes,
        idx
    )

# Visualize bad classifications
print("Bad Classifications:")
for idx in bad_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Bad Classification",
        activity_classes,
        idx
    )

# Visualize uncertain predictions
print("Uncertain Predictions:")
for idx in uncertain_indices[:3]:  # Show up to 3 examples
    seq, _ = val_loader.dataset[idx]
    plot_example(
        seq.numpy().T,
        true_labels[idx],
        predictions[idx],
        max(probabilities[idx]),
        "Uncertain Prediction",
        activity_classes,
    idx)

Validation: |          | 0/? [00:00<?, ?it/s]
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃      Validate metric      ┃       DataLoader 0        ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│          val_acc          │    0.44706466794013977    │
│         val_loss          │     1.447137475013733     │
└───────────────────────────┴───────────────────────────┘

Metrics saved to /kaggle/working/metrics_log.txt
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Good Classifications:
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Bad Classifications:
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Uncertain Predictions:
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Analysis of Results: Self-Supervised Masking LSTM (With and Without Improvements)¶

Performance Comparison¶

Metric Baseline LSTM LSTM with Implementing Suggestions Key Insights
Validation Accuracy 48.25% ~44% Accuracy decreased by ~4.25%.
Validation Loss ~1.4 ~1.42 Slightly higher loss indicates worse generalization.

Key Observations¶

  1. Validation Accuracy:

    • Accuracy decreased with the implementation of suggestions such as dropout and learning rate scheduling.
    • This decline may indicate that the dropout and scheduler parameters were suboptimal, leading to reduced model capacity or suboptimal learning rate adjustments.

  2. Validation Loss:

    • A minor increase in validation loss reflects slightly worse generalization compared to the baseline.
    • This suggests that the adjustments introduced regularization but may have hindered the model’s ability to capture complex activity patterns.

Comparative Analysis¶

  • While the implemented suggestions (dropout and learning rate scheduling) aim to reduce overfitting and stabilize training, their current configuration appears to have negatively impacted performance.
  • The results emphasize the importance of hyperparameter tuning for these techniques to achieve the desired balance between generalization and capacity.

Future Directions¶

  1. Hyperparameter Optimization:
    • Perform a systematic search to identify the optimal dropout rate and learning rate scheduler parameters.
  2. Enhanced Regularization Techniques:
    • Explore alternative regularization methods, such as L2 regularization or adaptive dropout rates.
  3. Data Augmentation:
    • Address dataset imbalance and variability with synthetic data generation or time-series augmentation.
  4. Architectural Enhancements:
    • Introduce attention mechanisms or gated recurrent units (GRUs) to improve model focus on critical features.

Key Takeaways¶

  • The Self-Supervised Masking LSTM with Implemented Suggestions demonstrates that regularization and scheduling require careful tuning to balance overfitting and underfitting.
  • Future efforts should focus on fine-tuning these techniques and incorporating advanced architectural features to improve performance.
In [ ]:
from nbconvert import HTMLExporter
import nbformat

# Load the notebook
with open("deep_learning_workshop_1_213138787_213479686_(3) (1).ipynb", "r", encoding="utf-8") as f:
    notebook_content = nbformat.read(f, as_version=4)

# Convert to HTML
html_exporter = HTMLExporter()
html_content, _ = html_exporter.from_notebook_node(notebook_content)

# Save HTML to file
with open("deep_learning_workshop_1_213138787_213479686_(3) (1).html", "w", encoding="utf-8") as f:
    f.write(html_content)

print("Notebook converted to HTML successfully!")